Systems and methods for managing vehicle operator profiles based on telematics inferences via an auction telematics marketplace with a bid profit predictive model

ABSTRACT

Method, system, device, and non-transitory computer-readable medium for data management. In some examples, a computer-implemented method includes: collecting a plurality of personal data sets and a plurality of sensor data sets; for each vehicle operator of the plurality of vehicle operators: generating and continually updating an operator profile, one or more telematics inferences, a data profile; and listing and continually updating the data profile onto a telematics marketplace; receiving a plurality of conditional bids for a target operator profile associated with a target data profile, each conditional bid of the plurality of conditional bids including one or more conditional payments and one or more payment conditions; determining, for each conditional bid of the plurality of conditional bids, a predicted bid-generated profit or a predicted bid-generated revenue; determining a winning bid and an associated winning bidder; and transmitting the target operator profile to the winning bidder.

CROSS-REFERENCES TO RELATED APPLICATIONS

The following eight applications, including this one, are being filedconcurrently and the other seven are hereby incorporated by reference intheir entirety for all purposes:

1. U.S. patent application Ser. No. ______, titled “SYSTEMS AND METHODSFOR MANAGING VEHICLE OPERATOR PROFILES BASED ON INCREMENTAL TELEMATICSINFERENCES VIA A TELEMATICS MARKETPLACE” (Attorney Docket NumberBOL-00008K-NP1);

2. U.S. patent application Ser. No. ______, titled “SYSTEMS AND METHODSFOR MANAGING VEHICLE OPERATOR PROFILES BASED ON RELATIVE TELEMATICSINFERENCES VIA A TELEMATICS MARKETPLACE” (Attorney Docket NumberBOL-00008L-NP1);

3. U.S. patent application Ser. No. ______, titled “SYSTEMS AND METHODSFOR MANAGING VEHICLE OPERATOR PROFILES BASED CHARACTERIZATION-SPECIFICTELEMATICS INFERENCES VIA A TELEMATICS MARKETPLACE” (Attorney DocketNumber BOL-00008M-NP1);

4. U.S. patent application Ser. No. ______, titled “SYSTEMS AND METHODSFOR INCREASING PROFITABILITY OF USER MANAGEMENT MODELS BASED ONTELEMATICS INFERENCES VIA A TELEMATICS MARKETPLACE” (Attorney DocketNumber BOL-00008N-NP1);

5. U.S. patent application Ser. No. ______, titled “SYSTEMS AND METHODSFOR USER MANAGEMENT MODEL TESTING BASED ON TELEMATICS INFERENCES VIA ATELEMATICS MARKETPLACE” (Attorney Docket Number BOL-000080-NP1);

6. U.S. patent application Ser. No. ______, titled “SYSTEMS AND METHODSFOR MANAGING VEHICLE OPERATOR PROFILES BASED ON TELEMATICS INFERENCESVIA AN AUCTION TELEMATICS MARKETPLACE WITH CONDITIONAL BIDDING”(Attorney Docket Number BOL-00008P-NP1);

7. U.S. patent application Ser. No. ______, titled “SYSTEMS AND METHODSFOR MANAGING VEHICLE OPERATOR PROFILES BASED ON TELEMATICS INFERENCESVIA AN AUCTION TELEMATICS MARKETPLACE WITH A BID PROFIT PREDICTIVEMODEL” (Attorney Docket Number BOL-00008Q-NP1); and

8. U.S. patent application Ser. No. ______, titled “SYSTEMS AND METHODSFOR MATCH EVALUATION BASED ON CHANGE IN TELEMATICS INFERENCES VIA ATELEMATICS MARKETPLACE” (Attorney Docket Number BOL-00008R-NP1).

U.S. Patent Application No. 63/049,052 is hereby incorporated byreference in their entirety for all purposes.

The following ten applications are hereby incorporated by reference intheir entirety for all purposes:

1. International PCT Application No. PCT/US2021/040272, filed Jul. 2,2021;

2. International PCT Application No. PCT/US2021/040282, filed Jul. 2,2021;

3. International PCT Application No. PCT/US2021/040291, filed Jul. 2,2021;

4. International PCT Application No. PCT/US2021/040295, filed Jul. 2,2021;

5. International PCT Application No. PCT/US2021/040308, filed Jul. 2,2021;

6. International PCT Application No. PCT/US2021/040314, filed Jul. 2,2021;

7. International PCT Application No. PCT/US2021/040317, filed Jul. 2,2021;

8. International PCT Application No. PCT/US2021/040320, filed Jul. 2,2021;

9. International PCT Application No. PCT/US2021/040325, filed Jul. 2,2021; and

10. International PCT Application No. PCT/US2021/040329, filed Jul. 2,2021.

FIELD OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to management ofuser information. More particularly, certain embodiments of the presentdisclosure provide systems and methods for managing vehicle operatorprofiles based on telematics inferences via an auction telematicsmarketplace with bid profit prediction. Merely by way of example, thepresent disclosure has been applied to management of user informationusing a telematics-data-based marketplace, but it would be recognizedthat the present disclosure has much broader range of applicability.

BACKGROUND

Conventional telematics data are often collected using party-specificdevices and for the sole use of that party. Customers of the party areoften asked by the party to install the party-specific device such thattelematics data of the customer can be collected. If a customer isinterested in exploring products of various parties, it is oftenrequired that the customer collect and install multiple party-specificdevices, one after another, sequentially, such that each party maycollect telematics using their corresponding party-specific device.There is a need for systems and methods for collecting and sharing oftelematics data with improved universality.

BRIEF SUMMARY OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to management ofuser information. More particularly, certain embodiments of the presentdisclosure provide systems and methods for managing vehicle operatorprofiles based on telematics inferences via an auction telematicsmarketplace with bid profit prediction. Merely by way of example, thepresent disclosure has been applied to management of user informationusing a telematics-data-based marketplace, but it would be recognizedthat the present disclosure has much broader range of applicability.

According to various embodiments, a computer-implemented method for datamanagement includes: collecting a plurality of personal data setsassociated with a plurality of vehicle operators continually; collectinga plurality of sensor data sets associated with the plurality of vehicleoperators continually via one or more sensing modules; for each vehicleoperator of the plurality of vehicle operators: generating andcontinually updating an operator profile including the personal data setassociated with the vehicle operator; determining and continuallyupdating one or more telematics inferences based at least in part uponthe sensor data set associated with the vehicle operator; generating andcontinually updating a data profile including the one or more telematicsinferences associated with the vehicle operator; and listing andcontinually updating the data profile onto a telematics marketplace tobe accessible by a plurality of marketplace participants; receiving,from a plurality of bidders of the plurality of marketplaceparticipants, a plurality of conditional bids for a target operatorprofile associated with a target data profile selected from the listeddata profiles of the plurality of vehicle operators, each conditionalbid of the plurality of conditional bids including one or moreconditional payments and one or more payment conditions; determining,for each conditional bid of the plurality of conditional bids, apredicted bid-generated profit or a predicted bid-generated revenue;determining, based at least in part upon the predicted profit orpredicted bid-generated revenue, a winning bid and an associated winningbidder; and transmitting the target operator profile to the winningbidder.

According to various embodiments, a computing system for datamanagement, the computing system includes: one or more processors; and amemory storing instructions that, upon execution by the one or moreprocessors, cause the computing system to perform one or more processesincluding: collecting a plurality of personal data sets associated witha plurality of vehicle operators continually; collecting a plurality ofsensor data sets associated with the plurality of vehicle operatorscontinually via one or more sensing modules; for each vehicle operatorof the plurality of vehicle operators: generating and continuallyupdating an operator profile including the personal data set associatedwith the vehicle operator; determining and continually updating one ormore telematics inferences based at least in part upon the sensor dataset associated with the vehicle operator; generating and continuallyupdating a data profile including the one or more telematics inferencesassociated with the vehicle operator; and listing and continuallyupdating the data profile onto a telematics marketplace to be accessibleby a plurality of marketplace participants; receiving, from a pluralityof bidders of the plurality of marketplace participants, a plurality ofconditional bids for a target operator profile associated with a targetdata profile selected from the listed data profiles of the plurality ofvehicle operators, each conditional bid of the plurality of conditionalbids including one or more conditional payments and one or more paymentconditions; determining, for each conditional bid of the plurality ofconditional bids, a predicted bid-generated profit or a predictedbid-generated revenue; determining, based at least in part upon thepredicted profit or predicted bid-generated revenue, a winning bid andan associated winning bidder; and transmitting the target operatorprofile to the winning bidder.

According to various embodiments, a non-transitory computer-readablemedium storing instructions for data management, the instructions uponexecution by one or more processors of a computing system, cause thecomputing system to perform one or more processes including: collectinga plurality of personal data sets associated with a plurality of vehicleoperators continually; collecting a plurality of sensor data setsassociated with the plurality of vehicle operators continually via oneor more sensing modules; for each vehicle operator of the plurality ofvehicle operators: generating and continually updating an operatorprofile including the personal data set associated with the vehicleoperator; determining and continually updating one or more telematicsinferences based at least in part upon the sensor data set associatedwith the vehicle operator; generating and continually updating a dataprofile including the one or more telematics inferences associated withthe vehicle operator; and listing and continually updating the dataprofile onto a telematics marketplace to be accessible by a plurality ofmarketplace participants; receiving, from a plurality of bidders of theplurality of marketplace participants, a plurality of conditional bidsfor a target operator profile associated with a target data profileselected from the listed data profiles of the plurality of vehicleoperators, each conditional bid of the plurality of conditional bidsincluding one or more conditional payments and one or more paymentconditions; determining, for each conditional bid of the plurality ofconditional bids, a predicted bid-generated profit or a predictedbid-generated revenue; determining, based at least in part upon thepredicted profit or predicted bid-generated revenue, a winning bid andan associated winning bidder; and transmitting the target operatorprofile to the winning bidder.

Depending upon the embodiment, one or more benefits may be achieved.These benefits, features, and advantages of the present disclosure canbe fully appreciated with reference to the detailed description andaccompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified diagram showing a telematics data marketplace(TDM) computing system according to various embodiments of the presentdisclosure.

FIG. 2 is a simplified diagram showing a computing system for managingvehicle operator profiles based on telematics inferences via an auctiontelematics marketplace with bid profit prediction according to variousembodiments of the present disclosure.

FIG. 3 is a simplified diagram showing a computer-implemented method formanaging vehicle operator profiles based on telematics inferences via anauction telematics marketplace with bid profit prediction according tovarious embodiments of the present disclosure.

FIG. 4 is a simplified diagram showing a computer device, according tovarious embodiments of the present disclosure.

FIG. 5 is a simplified diagram showing a computing system, according tovarious embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to management ofuser information. More particularly, certain embodiments of the presentdisclosure provide systems and methods for managing vehicle operatorprofiles based on telematics inferences via an auction telematicsmarketplace with bid profit prediction. Merely by way of example, thepresent disclosure has been applied to management of user informationusing a telematics-data-based marketplace, but it would be recognizedthat the present disclosure has much broader range of applicability.

One or More Systems for Providing a Telematics Data MarketplaceAccording to Various Embodiments

FIG. 1 is a simplified diagram showing a telematics data marketplace(TDM) computing system 100 according to various embodiments of thepresent disclosure. This figure is merely an example, which should notunduly limit the scope of the claims. One of ordinary skill in the artwould recognize many variations, alternatives, and modifications. Insome examples, the system 100 includes TDM computing system 102, adatabase (DB) 104, one or more user devices 106, and one or moreprovider devices 108. In certain examples, the system 100 is configuredto implement method 300 of FIG. 3 . Although the above has been shownusing a selected group of components, there can be many alternatives,modifications, and variations. In some examples, some of the componentsmay be expanded and/or combined. Some components may be removed. Othercomponents may be inserted to those noted above. Depending upon theembodiment, the arrangement of components may be interchanged withothers replaced.

In various embodiments, the TDM computing system 102 includes a databaseserver 105 configured to be communicatively coupled to the database 104to store and/or retrieve data. In some examples, the TDM computingsystem 102 is configured to be in communication with the one or moreuser devices 106. In some examples, the TDM computing system 102 isconfigured to be in communication with the one or more provider devices108 to receive insurance offers. In certain examples, the TDM computingsystem 102 is configured to receive user data (e.g., geographiccoordinate data, time measurement data, and/or telematics data) from theone or more user devices 106 and/or from the database 104. In variousembodiments, the database 104 includes a local storage device or aremote storage device, such as cloud storage. In various examples, theTDM computing system 102 may broker a deal between a user, associatedwith a user device 106 and a provider, associated with a provider device108, and the provider may offer reduced vehicle insurance premiums as areward for access to user data. In some examples, the TDM computingsystem 102 may restrict access to user data for certain providers. Forexample, a user may specify that certain providers are not permitted topurchase user data of said user such that the TDM computing system 102may restrict those providers from accessing said user data. In certainexamples, a user may grant or deny access to one or more providersthrough an associated user device 106.

In various embodiments, each user device of the one or more user devices106 includes a web browser and/or a software application for accessingthe TDM computing system 102, such as via a wired or wirelessconnection. For example, the one or more user devices 106 may becommunicatively coupled to TDM computing system 102 through theInternet, a local area network (LAN), a wide area network (WAN), anintegrated services digital network (ISDN), a dial-up-connection, adigital subscriber line (DSL), a cellular phone connection, and/or acable modem. The one or more user devices 106 may include a desktopcomputer, a laptop computer, a smartphone, a tablet, and/or a wearabledevice. In some examples, each user device of the one or more userdevices 106 includes a GPS sensor, an accelerometer, and/or a gyroscope.In certain examples, the one or more user devices 106 may collect userdata, such as geographic coordinate data, time measurement data, and/ortelematics data.

In some examples, the GPS sensor may utilize GPS techniques to determinea measurement of geographic coordinates of a corresponding user device106. The GPS sensor may provide real-time and/or historic navigationdata. The GPS sensor may return an error estimate along with themeasured geographic location. The measured geographic location and theerror estimate may provide an area (e.g., a radius around the measuredgeographic location) where the corresponding user device 106 may belocated with a probability value. In some examples, the accelerometermay be configured to measure a linear and/or angular acceleration of acorresponding user device 106 at a given moment in time. In someexamples, the gyroscope may be configured to determine an orientation ofan associated user device 106. In some examples, the accelerometer andthe gyroscope together may be used to determine a direction ofacceleration of the associated user device 106. In various examples,data generated by the GPS sensor, accelerometer, and/or gyroscope may beused (e.g., by TDM computing system 102 and/or user devices 106) togenerate telematics data (e.g., a location, orientation, acceleration,velocity, etc.) of the corresponding user device 106. In certainexamples, such telematics data may be provided to providers (e.g.,associated with provider devices 108, shown in FIG. 1 ) by the TDMcomputing system 102, for example, in exchange for a reward to the usersassociated with the one or more user devices 106.

In various embodiments, each provider device of the one or more providerdevices 108 includes a web browser and/or a software application foraccessing the TDM computing system 102, such as via a wired or wirelessconnection. For example, the one or more provider devices 108 may becommunicatively coupled to TDM computing system 102 through theInternet, a local area network (LAN), a wide area network (WAN), anintegrated services digital network (ISDN), a dial-up-connection, adigital subscriber line (DSL), a cellular phone connection, and/or acable modem. The one or more provider devices 108 may include a desktopcomputer, a laptop computer, a smartphone, a tablet, and/or a wearabledevice.

In various embodiments, the one or more provider devices 108 isconfigured to transmit one or more offers to the TDM computing system102. In some examples, the one or more offers includes a list of desireduser data and an accompanying purchase price. In certain examples, thepurchase price is in the form of a rewards points credit, a cash amount,a gift card, a charitable contribution amount, or a carbon offset creditamount. For example, a provider may specify, via an associated providerdevice, location-based data, number of users, and cash reward. Asanother example, a provider may specify, via an associated providerdevice, location-based data and time measurement data, number of users,and carbon offset credit.

In various embodiments, each user device includes one or more sensingmodules 110 configured to at least collect sensor data associated withthe user device. In some examples, the one or more sensing modules 110includes a common module used by a plurality of mobile applications. Insome examples, the common module is a software module or a commonhardware module. In some examples, each vehicle operator uses at leastone mobile application of the plurality of mobile applications. In someexamples, the plurality of mobile applications includes a systemsoftware application, an entertainment software application, a gamingsoftware application, a navigation software application, and/or anenvironment software application.

In various embodiments, the system further includes a conditionalbidding module 112 configured to provide one or more auction functionsto a telematics data based marketplace. In various examples, theconditional bidding module 112 is configured to receive, such as from aplurality of bidders that may or may not be marketplace participants ofan telematics auction marketplace, a plurality of conditional bids. Insome examples, the plurality of conditional bids are for a targetoperator profile associated with a target data profile. In certainexamples, the target data profile may be selected from listed dataprofiles of a plurality of vehicle operators. In some examples, eachconditional bid includes one or more conditional payments and associatedone or more payment conditions. In various examples, the conditionalbidding module 112 is further configured to determine a winning bid andan associated winning bidder based at least in part upon the pluralityof conditional bids. In certain examples, the conditional bidding module112 is further configured to transmit the target operator profile to thewinning bidder.

In some examples, the conditional bidding module 112 is configured tocollect, such as continually, management data of a target vehicleoperator associated with the target operator profile. In certainexamples, the conditional bidding module 112 is configured to determinewhether each payment condition of the one or more payment conditions issatisfied based at least in part upon the user management data. In someexamples, the one or more conditional payments includes a firstconditional payment and a second conditional payment. In some examples,the one or more payment conditions includes a first payment conditionand a second payment condition. In some examples, the first conditionalpayment is withheld from completion (e.g., transfer from winning bidderto telematics marketplace) at least until the first payment condition issatisfied. In some examples, the second conditional payment is withheldfrom completion (e.g., transfer from winning bidder to telematicsmarketplace) at least until the first payment condition and the secondpayment condition are satisfied.

In various examples, a payment condition may be bidding-related and maybe satisfied when a bidder is selected as the winning bidder. In variousexamples, a payment condition may be data transmission-related and maybe satisfied when a winning bidder receives the target operator profilethat the bidder won. In various examples, a payment condition may beuser acquisition-related and may be satisfied when the winning biddersuccessfully acquires a target vehicle operator associated with a targetoperator profile as a user (e.g., of a product of the winning bidder).In various examples, a payment condition may be user retention-relatedand may be satisfied when the winning bidder successfully retain atarget vehicle operator associated with the target operator profile as auser beyond a target time duration. In various examples, a paymentcondition may be profitability-related and may be satisfied when thewinning bidder successfully achieves a target profitability off from atarget vehicle operator associated with the target operator profile as auser. For example, the target profitability may be set as a breakevenpoint where the cost of acquiring the user and/or is completely offsetby profits generated by having the user as a customer.

In various embodiments, the system further includes a bid profitpredictive module 114 configured to determine, for each bid of theplurality of conditional bids, a predicted bid-generated profit. In someexamples, the bid profit predictive module 114 is configured todetermine the winning bid as the bid which has the highest predictedbid-generated profit. In some examples, the bid profit predictive module114 is configured to determine a predicted user retention duration. Insome examples, the bid profit predictive module 114 is configured todetermine the winning bid as the bid of the plurality of conditionalbids which has the highest predicted long-term bid-generated profit forthe full duration of the predicted user pretention duration. In someexamples, the bid profit predictive module 114 is configured todetermine the winning bid as the bid of the plurality of conditionalbids which has the highest predicted period-specific bid-generatedprofit for a period of interest predetermined by a marketplace entity.In some examples, the bid profit predictive module 114 is configured todetermine, for each payment condition of the one or more paymentconditions, a likelihood of condition fulfillment. In some examples, thebid profit predictive module 114 is configured to assign, for eachpayment condition of the one or more payment conditions, a weightmodifier. In various examples, the bid profit predictive module 114 isconfigured to assign a higher weight for conditions that are early(e.g., closer to the bidding process in time) and a lower weight forconditions that are late (e.g., farther from the bidding process intime).

In some examples, the bid profit predictive module 114 is configured todetermine a predicted bid-generated revenue and a predictedbid-generated costs. In some examples, the bid profit predictive module114 is configured to determine a predicted bid-generated revenue by atleast multiplying, for each payment condition of the one or more paymentconditions, the likelihood of success, the weight modifier, and anassociated conditional payment, and summing up the products of themultiplications. In some examples, the bid profit predictive module 114is configured to determine the winning bid to be the bid that has thehighest predicted bid-generated revenue. In some examples, the bidprofit predictive module 114 is configured to determine a predictedbid-generated costs for each payment condition of the one or morepayment conditions. For example, a predicted bid-generated costs mayinclude a predicted verification cost associated with verifying whethera condition has been satisfied and/or a predicted management costassociated with collecting, storing, and/or processing of usermanagement data. In some examples, the bid profit predictive module 114is configured to determine a predicted bid-generated profit bysubtracting the predicted bid-generated revenue with the predictedbid-generated cost.

One or More Systems for Managing Vehicle Operator Profiles Based onTelematics Inferences Via an Auction Telematics Marketplace with BidProfit Predicting According to Various Embodiments

FIG. 2 is a simplified diagram showing a system 200 for data managementusing bid profit predicting, according to various embodiments of thepresent disclosure. This figure is merely an example, which should notunduly limit the scope of the claims. One of ordinary skill in the artwould recognize many variations, alternatives, and modifications. Insome examples, the system 200 includes a personal data collecting module202, a sensor data collecting module 204, an operator profile generatingand updating module 206, a telematics inferences determining andupdating module 208, a data profile generating and updating module 210,a data profile listing and updating module 212, a conditional bidsreceiving module 214, a profit and revenue determining module 216, awinning bid determining module 218, and a target operator profiletransmitting module 218. In certain examples, the system 200 isconfigured to implement method 300 of FIG. 3 . In various examples, thesystem 200 includes one or more processors and a memory storinginstructions that, upon execution by the one or more processors, causethe computing system to perform one or more processes including one ormore processes of method 300. Although the above has been shown using aselected group of components, there can be many alternatives,modifications, and variations. In some examples, some of the componentsmay be expanded and/or combined. Some components may be removed. Othercomponents may be inserted to those noted above. Depending upon theembodiment, the arrangement of components may be interchanged withothers replaced.

In various embodiments, the personal data collecting module 202 isconfigured to collect a plurality of personal data sets associated witha plurality of vehicle operators continually. In some examples, personaldata set is collected via one or more marketplace participants. In someexamples, the one or more marketplace participants includes an insurancecompany, a car rental company, a vehicle manufacturing company, anautonomous driving firm, a shared ride company, a housing firm, a bank,and/or a government agency. In some examples, the personal data includesvehicle operator-answered questionnaire data, application-usage data,device-usage data, internet-browsing data, or government data. In someexamples, personal data include name, age, sex, gender, vehicleoperation history, geolocation, occupation, financial data,homeownership data, credit score, personal preferences, and/or personalvalues.

In various embodiments, the sensor data collecting module 204 isconfigured to collect a plurality of sensor data sets associated withthe plurality of vehicle operators continually via one or more sensingmodules. In some examples, the one or more sensing modules includes acommon module used by a plurality of mobile applications. In someexamples, the common module is a software module. In some examples, thecommon module is a common hardware module. In some examples, eachvehicle operator, such as of a plurality of vehicle operators, uses atleast one mobile application of the plurality of mobile applications. Insome examples, the plurality of mobile applications includes a systemsoftware application, an entertainment software application, a gamingsoftware application, a navigation software application, and/or anenvironment software application.

In various embodiments, the operator profile generating and updatingmodule 206 is configured to generate and continually update, such as foreach vehicle operator of a plurality of vehicle operators, an operatorprofile including the personal data set associated with the vehicleoperator.

In various embodiments, the telematics inferences determining andupdating module 208 is configured to determine and/or update, such ascontinually and/or for each vehicle operator of a plurality of vehicleoperators, one or more telematics inferences, such as based at least inpart upon the sensor data set associated with the vehicle operator. Insome examples, the telematics inferences determining and updating module208 is configured to determine and/or update, such as continually and/orfor each vehicle operator of a plurality of vehicle operators, one ormore telematics inferences, such as based at least in part upon thesensor data set and the personal data set associated with the vehicleoperator. In some examples, the telematics inferences determining andupdating module 208 is configured to determine and/or update, such ascontinually, a predicted profitability based at least in part upon theassociated continually received personal data set and/or the associatedcontinually received sensor data set. In some examples, the telematicsinferences determining and updating module 208 is configured todetermine and/or update, such as continually, the predictedprofitability using a predictive model, such as using a predictive modelhaving a plurality of weights and biases that correspond to theimportance of each type of sensor data in the determination of thepredicted profitability. In some examples, the telematics inferencesdetermining and updating module 208 is configured to determine and/orupdate, such as continually, a predicted costs and/or a predictedrevenue based at least in part upon the associated continually receivedpersonal data set and/or the associated continually received sensor dataset. In some examples, the one or more predictive models includes apredictive revenue model, a predictive costs model, a predictive lossesmodel, and/or a predictive expenses model. In some examples, the one ormore telematics inferences includes a profitability score, a reliabilityscore, a financial stability score, a financial reliability score, ademographic score, a mobility score, a predicted risk score, a predictedcosts score, a predicted retention score, and/or a payment reliabilityscore.

In various embodiments, the data profile generating and updating module210 is configured to generate and continually update a data profileincluding the one or more telematics inferences associated with thevehicle operator. In some examples, the data profile includes theassociated personal data set and/or sensor data set. In some examples,personal data include name, age, sex, gender, vehicle operation history,geolocation, occupation, financial data, homeownership data, creditscore, personal preferences, and/or personal values.

In various embodiments, the data profile listing and updating module 212is configured to list and continually update the data profile onto atelematics marketplace, such as to be accessible by a plurality ofmarketplace participants.

In various embodiments, the bid receiving module 214 is configured toreceive, such as from a plurality of bidders who are marketplaceparticipants (e.g., marketplace consumers), a plurality of conditionalbids for a target operator profile associated with a target dataprofile. In some examples, the target data profile may be selected froma list of listed data profiles on the telematics marketplace. In certainexamples, each listed data profile corresponds to a vehicle operator. Insome examples, each conditional bid includes one or more conditionalpayments and associated one or more payment conditions. In variousexamples, a payment condition may be bidding-related and may besatisfied when a bidder is selected as the winning bidder. In variousexamples, a payment condition may be data transmission-related and maybe satisfied when a winning bidder receives the target operator profilethat the bidder won. In various examples, a payment condition may beuser acquisition-related and may be satisfied when the winning biddersuccessfully acquires a target vehicle operator associated with a targetoperator profile as a user (e.g., of a product of the winning bidder).In various examples, a payment condition may be user retention-relatedand may be satisfied when the winning bidder successfully retain atarget vehicle operator associated with the target operator profile as auser beyond a target time duration. In various examples, a paymentcondition may be profitability-related and may be satisfied when thewinning bidder successfully achieves a target profitability off from atarget vehicle operator associated with the target operator profile as auser. For example, the target profitability may be set as a breakevenpoint where the cost of acquiring the user and/or is completely offsetby profits generated by having the user as a customer.

In various embodiments, the profit and revenue determining module 216 isconfigured to determine, for each bid of the plurality of conditionalbids, a predicted bid-generated profit. In some examples, the profit andrevenue determining module 216 is configured to determine a predicteduser retention duration. In some examples, the profit and revenuedetermining module 216 is configured to determine, for each paymentcondition of the one or more payment conditions, a likelihood ofcondition fulfillment. In some examples, the profit and revenuedetermining module 216 is configured to assign, for each paymentcondition of the one or more payment conditions, a weight modifier. Invarious examples, the profit and revenue determining module 216 isconfigured to assign a higher weight for conditions that are early(e.g., closer to the bidding process in time) and a lower weight forconditions that are late (e.g., farther from the bidding process intime).

In some examples, the profit and revenue determining module 216 isconfigured to determine a predicted bid-generated revenue and apredicted bid-generated costs. In some examples, the profit and revenuedetermining module 216 is configured to determine a predictedbid-generated revenue by at least multiplying, for each paymentcondition of the one or more payment conditions, the likelihood ofsuccess, the weight modifier, and an associated conditional payment, andsumming up the products of the multiplications. In some examples, theprofit and revenue determining module 216 is configured to determine apredicted bid-generated costs for each payment condition of the one ormore payment conditions. For example, a predicted bid-generated costsmay include a predicted verification cost associated with verifyingwhether a condition has been satisfied and/or a predicted managementcost associated with collecting, storing, and/or processing of usermanagement data. In some examples, the profit and revenue determiningmodule 216 is configured to determine a predicted bid-generated profitby subtracting the predicted bid-generated revenue with the predictedbid-generated cost.

In various embodiments, the winning bid determining module 218 isconfigured to determine a winning bid and an associated winning bidderbased at least in part upon the plurality of conditional bids. In someexamples, the winning bid determining module 218 is configured todetermine the winning bid as the bid which has the highest predictedbid-generated profit. In some examples, the winning bid determiningmodule 218 is configured to determine the winning bid as the bid of theplurality of conditional bids which has the highest predicted long-termbid-generated profit for the full duration of the predicted userpretention duration. In some examples, the winning bid determiningmodule 218 is configured to determine the winning bid as the bid of theplurality of conditional bids which has the highest predictedperiod-specific bid-generated profit for a period of interestpredetermined by a marketplace entity. In some examples, the winning biddetermining module 218 is configured to determine the winning bid to bethe bid that has the highest predicted bid-generated revenue.

In various embodiments, the target operator profile transmitting module220 is configured to transmit the target operator profile to the winningbidder.

In some examples, the system 200 is further configured to collect, suchas continually, management data of a target vehicle operator associatedwith the target operator profile. In certain examples, the system 200 isconfigured to determine whether each payment condition of the one ormore payment conditions is satisfied based at least in part upon theuser management data. In some examples, the one or more conditionalpayments includes a first conditional payment and a second conditionalpayment. In some examples, the one or more payment conditions includes afirst payment condition and a second payment condition. In someexamples, the first conditional payment is withheld from completion(e.g., transfer from winning bidder to telematics marketplace) at leastuntil the first payment condition is satisfied. In some examples, thesecond conditional payment is withheld from completion (e.g., transferfrom winning bidder to telematics marketplace) at least until the firstpayment condition and the second payment condition are satisfied.

One or More Methods for Managing Vehicle Operator Profiles Based onTelematics Inferences Via an Auction Telematics Marketplace with BidProfit Predicting According to Various Embodiments

FIG. 3 is a simplified method 300 for data management using conditionalbidding, according to various embodiments of the present disclosure.This figure is merely an example, which should not unduly limit thescope of the claims. One of ordinary skill in the art would recognizemany variations, alternatives, and modifications. The method 300includes a process a process 302 of collecting a plurality of personaldata sets continually, a process 304 of collecting a plurality of sensordata sets continually, a process 306 of generating and continuallyupdating an operator profile, a process 308 of determining andcontinually updating one or more telematics inferences, a process 310 ofgenerating and continually updating a data profile, a process 312 oflisting and continually updating the data profile onto a telematicsmarketplace, a process 314 of receiving a plurality of bids for a targetoperator profile, a process 316 of determining profit and revenue, aprocess 318 of determining a winning bid, and a process 320 oftransmitting the target operator profile according to award protocols.In certain examples, the method 300 is configured to be implemented bysystem 200 of FIG. 2 . Although the above has been shown using aselected group of processes for the method, there can be manyalternatives, modifications, and variations. In some examples, some ofthe processes may be expanded and/or combined. Other processes may beinserted to those noted above. Depending upon the embodiment, thesequence of processes may be interchanged with others replaced. In someexamples, some or all processes of the method are performed by acomputing system or a processor directed by instructions stored inmemory. As an example, some or all processes of the method are performedaccording to instructions stored in a non-transitory computer-readablemedium.

In various embodiments, the process 302 of collecting a plurality ofpersonal data sets continually includes collecting a plurality ofpersonal data sets associated with a plurality of vehicle operatorscontinually. In some examples, the personal data set includes vehicleoperator-answered questionnaire data, application-usage data,device-usage data, internet-browsing data, and/or government data. Insome examples, personal data include name, age, sex, gender, vehicleoperation history, geolocation, occupation, financial data,homeownership data, credit score, personal preferences, and/or personalvalues.

In various embodiments, the process 304 of collecting a plurality ofsensor data sets continually includes collecting a plurality of sensordata sets associated with the plurality of vehicle operators continuallyvia one or more sensing modules. In some examples, the one or moresensing modules includes a common module used by a plurality of mobileapplications. In some examples, the common module is a software moduleor a common hardware module. In some examples, each vehicle operatoruses at least one mobile application of the plurality of mobileapplications. In some examples, the plurality of mobile applicationsincludes a system software application, an entertainment softwareapplication, a gaming software application, a navigation softwareapplication, and/or an environment software application.

In various embodiments, the process 306 of generating and continuallyupdating an operator profile includes generating and continuallyupdating, such as for each vehicle operator of the plurality of vehicleoperators, an operator profile including the personal data setassociated with the vehicle operator.

In various embodiments, the process 308 of determining and continuallyupdating one or more telematics inferences includes determining andcontinually updating, such as for each vehicle operator of the pluralityof vehicle operators, one or more telematics inferences based at leastin part upon the sensor data set associated with the vehicle operator.In some examples, the process 308 of determining and continuallyupdating one or more telematics inferences includes determining andcontinually updating a predicted profitability based at least in partupon the associated continually received personal data set and theassociated continually received sensor data set. In some examples, thedetermining and continually updating the predicted profitabilityincludes determining and continually updating the predictedprofitability using a predictive model having a plurality of weights andbiases that correspond to the importance of each type of sensor data inthe determination of the predicted profitability. In some examples, thedetermining and continually updating the predicted profitabilityincludes determining and continually updating a predicted costs and apredicted revenue based at least in part upon the associated continuallyreceived personal data set and the associated continually receivedsensor data set. In some examples, the one or more telematics inferencesincludes a profitability score, a reliability score, a financialstability score, a financial reliability score, a demographic score, amobility score, a predicted risk score, a predicted costs score, apredicted retention score, and/or a payment reliability score.

In various embodiments, the process 310 of generating and continuallyupdating, such as for each vehicle operator of the plurality of vehicleoperators, a data profile includes generating and continually updating adata profile including the one or more telematics inferences associatedwith the vehicle operator.

In various embodiments, the process 312 of listing and continuallyupdating, such as for each vehicle operator of the plurality of vehicleoperators, the data profile onto a telematics marketplace includeslisting and continually updating the data profile onto a telematicsmarketplace to be accessible by a plurality of marketplace participants.In some examples, the plurality of marketplace participants includes aninsurance company, a car rental company, a vehicle manufacturingcompany, an autonomous driving firm, a shared ride company, a housingfirm, a bank, and/or a government agency.

In various embodiments, the process 314 of receiving a plurality ofconditional bids for a target operator profile includes receiving, suchas from a plurality of bidders who are marketplace participants (e.g.,marketplace consumers), a plurality of conditional bids for a targetoperator profile associated with a target data profile. In someexamples, the target data profile may be selected from a list of listeddata profiles on the telematics marketplace. In certain examples, eachlisted data profile corresponds to a vehicle operator. In some examples,each conditional bid includes one or more conditional payments andassociated one or more payment conditions. In various examples, apayment condition may be bidding-related and may be satisfied when abidder is selected as the winning bidder. In various examples, a paymentcondition may be data transmission-related and may be satisfied when awinning bidder receives the target operator profile that the bidder won.In various examples, a payment condition may be user acquisition-relatedand may be satisfied when the winning bidder successfully acquires atarget vehicle operator associated with a target operator profile as auser (e.g., of a product of the winning bidder). In various examples, apayment condition may be user retention-related and may be satisfiedwhen the winning bidder successfully retain a target vehicle operatorassociated with the target operator profile as a user beyond a targettime duration. In various examples, a payment condition may beprofitability-related and may be satisfied when the winning biddersuccessfully achieves a target profitability off from a target vehicleoperator associated with the target operator profile as a user. Forexample, the target profitability may be set as a breakeven point wherethe cost of acquiring the user and/or is completely offset by profitsgenerated by having the user as a customer.

In various embodiments, the process 316 of determining profit andrevenue includes determining, for each bid of the plurality ofconditional bids, a predicted bid-generated profit. In some examples,the process 316 of determining profit and revenue includes determining apredicted user retention duration. In some examples, the process 316 ofdetermining profit and revenue includes determining, for each paymentcondition of the one or more payment conditions, a likelihood ofcondition fulfillment. In some examples, the process 316 of determiningprofit and revenue includes assigning, for each payment condition of theone or more payment conditions, a weight modifier. In various examples,the process 316 of determining profit and revenue includes assigning ahigher weight for conditions that are early (e.g., closer to the biddingprocess in time) and a lower weight for conditions that are late (e.g.,farther from the bidding process in time).

In some examples, the process 316 of determining profit and revenueincludes determining a predicted bid-generated revenue and a predictedbid-generated costs. In some examples, the process 316 of determiningprofit and revenue includes determining a predicted bid-generatedrevenue by at least multiplying, for each payment condition of the oneor more payment conditions, the likelihood of success, the weightmodifier, and an associated conditional payment, and summing up theproducts of the multiplications. In some examples, the process 316 ofdetermining profit and revenue includes determining a predictedbid-generated costs for each payment condition of the one or morepayment conditions. For example, a predicted bid-generated costs mayinclude a predicted verification cost associated with verifying whethera condition has been satisfied and/or a predicted management costassociated with collecting, storing, and/or processing of usermanagement data. In some examples, the process 316 of determining profitand revenue includes determining a predicted bid-generated profit bysubtracting the predicted bid-generated revenue with the predictedbid-generated cost.

In various embodiments, the process 318 of determining a winning bidincludes determining a winning bid and an associated winning bidderbased at least in part upon the plurality of conditional bids. In someexamples, the process 318 of determining a winning bid includesdetermining the winning bid as the bid which has the highest predictedbid-generated profit. In some examples, the process 318 of determining awinning bid includes determining the winning bid as the bid of theplurality of conditional bids which has the highest predicted long-termbid-generated profit for the full duration of the predicted userpretention duration. In some examples, the process 318 of determining awinning bid includes determining the winning bid as the bid of theplurality of conditional bids which has the highest predictedperiod-specific bid-generated profit for a period of interestpredetermined by a marketplace entity. In some examples, the process 318of determining a winning bid includes determining the winning bid to bethe bid that has the highest predicted bid-generated revenue.

In various embodiments, the process 320 of transmitting the targetoperator profile includes transmitting the target operator profile tothe winning bidder.

In some examples, the method 300 further includes collecting, such ascontinually, management data of a target vehicle operator associatedwith the target operator profile. In certain examples, the method 300further includes determining whether each payment condition of the oneor more payment conditions is satisfied based at least in part upon theuser management data. In some examples, the one or more conditionalpayments includes a first conditional payment and a second conditionalpayment. In some examples, the one or more payment conditions includes afirst payment condition and a second payment condition. In someexamples, the first conditional payment is withheld from completion(e.g., transfer from winning bidder to telematics marketplace) at leastuntil the first payment condition is satisfied. In some examples, thesecond conditional payment is withheld from completion (e.g., transferfrom winning bidder to telematics marketplace) at least until the firstpayment condition and the second payment condition are satisfied.

One or More Computer Devices According to Various Embodiments

FIG. 4 is a simplified diagram showing a computer device 5000, accordingto various embodiments of the present disclosure. This figure is merelyan example, which should not unduly limit the scope of the claims. Oneof ordinary skill in the art would recognize many variations,alternatives, and modifications. In some examples, the computer device5000 includes a processing unit 5002, a memory unit 5004, an input unit5006, an output unit 5008, and a communication unit 5010. In variousexamples, the computer device 5000 is configured to be in communicationwith a user 5100 and/or a storage device 5200. In certain examples, thesystem computer device 5000 is configured according to system 200 ofFIG. 2 and/or to implement method 300 of FIG. 3 . Although the above hasbeen shown using a selected group of components, there can be manyalternatives, modifications, and variations. In some examples, some ofthe components may be expanded and/or combined. Some components may beremoved. Other components may be inserted to those noted above.Depending upon the embodiment, the arrangement of components may beinterchanged with others replaced.

In various embodiments, the processing unit 5002 is configured forexecuting instructions, such as instructions to implement method 300 ofFIG. 3 . In some embodiments, executable instructions may be stored inthe memory unit 5004. In some examples, the processing unit 5002includes one or more processing units (e.g., in a multi-coreconfiguration). In certain examples, the processing unit 5002 includesand/or is communicatively coupled to one or more modules forimplementing the systems and methods described in the presentdisclosure. In some examples, the processing unit 5002 is configured toexecute instructions within one or more operating systems, such as UNIX,LINUX, Microsoft Windows®, etc. In certain examples, upon initiation ofa computer-implemented method, one or more instructions is executedduring initialization. In some examples, one or more operations isexecuted to perform one or more processes described herein. In certainexamples, an operation may be general or specific to a particularprogramming language (e.g., C, C#, C++, Java, or other suitableprogramming languages, etc.). In various examples, the processing unit5002 is configured to be operatively coupled to the storage device 5200,such as via an on-board storage unit 5012.

In various embodiments, the memory unit 5004 includes a device allowinginformation, such as executable instructions and/or other data to bestored and retrieved. In some examples, memory unit 5004 includes one ormore computer readable media. In some embodiments, stored in memory unit5004 include computer readable instructions for providing a userinterface, such as to the user 5004, via the output unit 5008. In someexamples, a user interface includes a web browser and/or a clientapplication. In various examples, a web browser enables one or moreusers, such as the user 5004, to display and/or interact with mediaand/or other information embedded on a web page and/or a website. Incertain examples, the memory unit 5004 include computer readableinstructions for receiving and processing an input, such as from theuser 5004, via the input unit 5006. In certain examples, the memory unit5004 includes random access memory (RAM) such as dynamic RAM (DRAM) orstatic RAM (SRAM), read-only memory (ROM), erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), and/or non-volatile RAM (NVRAN).

In various embodiments, the input unit 5006 is configured to receiveinput, such as from the user 5004. In some examples, the input unit 5006includes a keyboard, a pointing device, a mouse, a stylus, a touchsensitive panel (e.g., a touch pad or a touch screen), a gyroscope, anaccelerometer, a position detector (e.g., a Global Positioning System),and/or an audio input device. In certain examples, the input unit 5006,such as a touch screen of the input unit, is configured to function asboth the input unit and the output unit.

In various embodiments, the output unit 5008 includes a media outputunit configured to present information to the user 5004. In someembodiments, the output unit 5008 includes any component capable ofconveying information to the user 5004. In certain embodiments, theoutput unit 5008 includes an output adapter, such as a video adapterand/or an audio adapter. In various examples, the output unit 5008, suchas an output adapter of the output unit, is operatively coupled to theprocessing unit 5002 and/or operatively coupled to an presenting deviceconfigured to present the information to the user, such as via a visualdisplay device (e.g., a liquid crystal display (LCD), a light emittingdiode (LED) display, an organic light emitting diode (OLED) display, acathode ray tube (CRT) display, an “electronic ink” display, a projecteddisplay, etc.) or an audio display device (e.g., a speaker arrangementor headphones).

In various embodiments, the communication unit 5010 is configured to becommunicatively coupled to a remote device. In some examples, thecommunication unit 5010 includes a wired network adapter, a wirelessnetwork adapter, a wireless data transceiver for use with a mobile phonenetwork (e.g., Global System for Mobile communications (GSM), 3G, 4G,5G, NFC, or Bluetooth), and/or other mobile data networks (e.g.,Worldwide Interoperability for Microwave Access (WIMAX)). In certainexamples, other types of short-range or long-range networks may be used.In some examples, the communication unit 5010 is configured to provideemail integration for communicating data between a server and one ormore clients.

In various embodiments, the storage unit 5012 is configured to enablecommunication between the computer device 5000, such as via theprocessing unit 5002, and an external storage device 5200. In someexamples, the storage unit 5012 is a storage interface. In certainexamples, the storage interface is any component capable of providingthe processing unit 5002 with access to the storage device 5200. Invarious examples, the storage unit 5012 includes an Advanced TechnologyAttachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computingsystem Interface (SCSI) adapter, a RAID controller, a SAN adapter, anetwork adapter, and/or any other component capable of providing theprocessing unit 5002 with access to the storage device 5200.

In some examples, the storage device 5200 includes any computer-operatedhardware suitable for storing and/or retrieving data. In certainexamples, storage device 5200 is integrated in the computer device 5000.In some examples, the storage device 5200 includes a database, such as alocal database or a cloud database. In certain examples, the storagedevice 5200 includes one or more hard disk drives. In various examples,the storage device is external and is configured to be accessed by aplurality of server systems. In certain examples, the storage deviceincludes multiple storage units such as hard disks or solid state disksin a redundant array of inexpensive disks (RAID) configuration. In someexamples, the storage device 5200 includes a storage area network (SAN)and/or a network attached storage (NAS) system.

One or More Computing Systems According to Various Embodiments

FIG. 5 is a simplified computing system 7000 according to variousembodiments of the present disclosure. This figure is merely an example,which should not unduly limit the scope of the claims. One of ordinaryskill in the art would recognize many variations, alternatives, andmodifications. In some examples, the system 7000 includes a vehiclesystem 7002, a network 7004, and a server 7006. In certain examples, thesystem 7000, the vehicle system 7002, and/or the server 7006 isconfigured according to system 200 of FIG. 2 and/or to implement method300 of FIG. 3 . Although the above has been shown using a selected groupof components, there can be many alternatives, modifications, andvariations. In some examples, some of the components may be expandedand/or combined. Some components may be removed. Other components may beinserted to those noted above. Depending upon the embodiment, thearrangement of components may be interchanged with others replaced.

In various embodiments, the vehicle system 7002 includes a vehicle 7010and a client device 7012 associated with the vehicle 7010. In variousexamples, the client device 7012 is an on-board computer embedded orlocated in the vehicle 7010. As an example, the client device 7012 is amobile device (e.g., a smartphone) that is connected (e.g., via a wiredconnection or a wireless connection) to the vehicle 7010. In someexamples, the client device 7012 includes a processor 7016 (e.g., acentral processing unit (CPU), and/or a graphics processing unit (GPU)),a memory 7018 (e.g., storage unit, random-access memory (RAM), and/orread-only memory (ROM), flash memory), a communications unit 7020 (e.g.,a network transceiver), a display unit 7022 (e.g., a touchscreen), andone or more sensors 7024 (e.g., an accelerometer, a gyroscope, amagnetometer, and/or a GPS sensor).

In various embodiments, the vehicle 7010 is operated by a user. Incertain embodiments, the system 7000 includes multiple vehicles 7010,each vehicle of the multiple vehicles operated by a respective user ofmultiple users. In various examples, the one or more sensors 7024monitors, during one or more vehicle trips, the vehicle 7010 by at leastcollecting data associated with one or more operating parameters of thevehicle, such as speed, speeding, braking, location, engine status,and/or other suitable parameters. In certain examples, the collecteddata include vehicle telematics data. According to some embodiments, thedata are collected continuously, at predetermined time intervals, and/orbased on one or more triggering events (e.g., when a sensor has acquiredmeasurements greater than a threshold amount of sensor measurements). Invarious examples, the data collected by the one or more sensors 7024correspond to user driving data, which may correspond to a driver'sdriving behaviors, in the methods and/or systems of the presentdisclosure.

According to various embodiments, the collected data are stored in thememory 7018 before being transmitted to the server 7006 using thecommunications unit 7020 via the network 7004 (e.g., via a local areanetwork (LAN), a wide area network (WAN), or the Internet). In someexamples, the collected data are transmitted directly to the server 7006via the network 7004. In certain examples, the collected data aretransmitted to the server 7006 via a third party. In some examples, adata monitoring system, managed or operated by a third party, isconfigured to store data collected by the one or more sensors 7024 andto transmit such data to the server 7006 via the network 7004 or adifferent network.

According to various embodiments, the server 7006 includes a processor7030 (e.g., a microprocessor, a microcontroller), a memory 7032 (e.g., astorage unit), a communications unit 7034 (e.g., a network transceiver),and a data storage 7036 (e.g., one or more databases). In some examples,the server 7006 is a single server, while in certain embodiments, theserver 7006 includes a plurality of servers with distributed processingand/or storage. In certain examples, the data storage 7036 is part ofthe server 7006, such as coupled via a network (e.g., the network 7004).In some examples, data, such as processed data and/or results, may betransmitted from the data storage, such as via the communications unit7034, the network 7004, and/or the communications unit 7020, to theclient device 7012, such as for display by the display 7022.

In some examples, the server 7006 includes various software applicationsstored in the memory 7032 and executable by the processor 7030. In someexamples, these software applications include specific programs,routines, and/or scripts for performing functions associated with themethods of the present disclosure. In certain examples, the softwareapplications include general-purpose software applications for dataprocessing, network communication, database management, web serveroperation, and/or other functions typically performed by a server. Invarious examples, the server 7006 is configured to receive, such as viathe network 7004 and via the communications unit 7034, the datacollected by the one or more sensors 7024 from the client device 7012,and stores the data in the data storage 7036. In some examples, theserver 7006 is further configured to process, via the processor 7030,the data to perform one or more processes of the methods of the presentdisclosure.

Examples of Certain Embodiments of the Present Disclosure

In various embodiments, systems and methods of the present disclosureprovide a marketplace configured to automatically match a customerseeking insurance to one or more insurance policy offers based at leastin part upon the customer's associated telematics data. In someexamples, said telematics data are collected via a mobile deviceassociated with the customer, such as via one or more softwareapplications installed on the mobile device. In certain examples, theone or more software applications includes a system softwareapplication, an entertainment software application, a gaming softwareapplication, a navigation software application, and/or an environmentsoftware application. In various examples, telematics data of a firstcustomer may be collected via a first selection of softwareapplication(s) from the one or more software applications, whereastelematics data of a second customer may be collected via a secondselection of software application(s) from the one or more softwareapplications. Telematics data collected via the first selection ofsoftware application(s) or the second selection of softwareapplication(s) may be used by the marketplace for automatically matchinga policy offer to a customer.

In various embodiments, systems and methods of the present disclosureprovide a marketplace configured to provide telematics data and/orinferences that are industry-specific, market-specific, and/oruse-specific. Telematics inferences may include scores, ratings,insights, guidance and recommendation, and/or calculation results. Forexample, a user of the marketplace may be in the auto insurance industryand may receive auto-insurance-related score(s), rating(s), insight(s),recommendation(s), and/or calculation result(s) transmitted by themarketplace. The marketplace may determine the industry-specificscore(s), rating(s), insight(s), recommendation(s), and/or calculationresult(s) using one or more industry-specific algorithms, such as onesprovided by the industry user(s). The industry-specific algorithms mayinclude weights and biases that correspond to the importance of eachtype of telematics data associated with a plurality of vehicleoperators. As another example, the user of the marketplace may be in thebanking industry and may receive credit-worthiness score(s), rating(s),insight(s), recommendation(s), and/or calculation result(s) transmittedby the marketplace. The marketplace may determine the use-specificscore(s), rating(s), insight(s), recommendation(s), and/or calculationresult(s) using one or more use-specific algorithms, such as onesprovided by the user(s) having a specific use. The use-specificalgorithms may include weights and biases that correspond to theimportance of each type of telematics data associated with a pluralityof vehicle operators.

In various embodiments, systems and methods of the present disclosurecollect telematics data associated with a plurality of vehicle operatorsusing one or more software and/or hardware modules, such as of asoftware application installed on a portable device associated with eachvehicle operator of the plurality of vehicle operators. In someexamples, the plurality of vehicle operators may be users and/or clientsof one or more insurance companies, one or more banks, one or morehealth insurers, one or more rental companies, one or more vehiclemanufacturers, one or more ride-share companies, one or more housingfirms, and/or one or more autonomous driving companies. In certainexamples, each vehicle operator of the plurality of vehicle operatorsmay be provided with the hardware module and/or software moduleconfigured to collect telematics data from an associated insurancecompany, an associated bank, an associated health insurer, an associatedrental company, an associated vehicle manufacturer, an associatedride-share company, an associated housing firm, and/or an associatedautonomous driving company.

In various embodiments, systems and methods of the present disclosureprovide a universal marketplace for a plurality of users, a plurality ofclients, a plurality of vehicle operators, a plurality of subscribers,and/or a plurality of members for collecting, scoring, storing,managing, and/or sharing telematics data and telematics-based inferencesregarding the plurality of vehicle operators. In some examples, thesystems and methods of the present disclosure provide the universalmarketplace using continual, such as real-time or near-real-time,collecting of telematics data, determining of telematics-basedinferences, and/or presenting of telematics data and/or telematics-basedinferences. In other examples, the systems and methods of the presentdisclosure provide the universal marketplace using intermittent, such asby following a pre-determined scheduled, collecting of telematics data,determining of telematics-based inferences, and/or presenting oftelematics data and/or telematics-based inferences.

In various embodiments, systems and methods of the present disclosurecollect telematics data via a software development kit (SDK), such asvia a common software development kit installed as part of a pluralityof software applications. In various examples, each mobile device of aplurality of mobile devices (e.g., phones, vehicles, and/or portableunits) of a plurality of vehicle operators may be loaded with one ormore software applications of the plurality of software applications.Each software application of the one or more software applications maycollect, via one or more hardware modules associated with a vehicleoperated by the associated vehicle operator, one or more types oftelematics data (e.g., acceleration, braking, cornering). In certainexamples, the common SDK may configure a first software application tocollect a first set of telematics data and configure a second softwareapplication to collect a second set of telematics data, where both thefirst set of telematics data and the second set of telematics data maybe combined complimentarily to describe driving behaviors of acorresponding vehicle operator during a corresponding time period. Insome examples, systems and methods of the present disclosure collectstelematics data, such as via a plurality of software applications havinga common SDK such that the telematics data collected are in standardizedformat(s) such that the marketplace may process the telematics data on aconsistent basis. In some examples, a plurality of software applicationsmay include a common SDK configured to enable background-locationtracking, which when enabled, collects at least location-basedtelematics data for the associated vehicle operator(s).

In various embodiments, systems and methods of the present disclosureshare and/or transmit information (e.g., telematics data,telematics-data-based inferences such as score(s), rating(s),insight(s), recommendation(s), and/or calculation result(s)) withmarketplace participants (e.g., users, clients, and/or subscribers) in astandardized or universally accepted data format(s) and deliveryprotocol(s) (e.g., with one or more security features to ensure datasecurity and/or privacy). As such, the information may be populatedconsistently to a plurality of marketplace participants of a pluralityof industries having a plurality of uses for the data.

In various embodiments, systems and methods of the present disclosureprovide a shared telematics data-based marketplace to be accessed, suchas via a subscription or authentication requirement, by a plurality ofmarketplace participants. The plurality of marketplace participants mayprovide input data and/or algorithm(s) to the determining module(s) ofthe marketplace to obtain telematics-data-based inferences such asscore(s), rating(s), insight(s), recommendation(s), and/or calculationresult(s). In some examples, none or some or all of the input dataprovided by a marketplace participant are shared with none or some orall of the other marketplace participants of the plurality ofmarketplace participants. In some examples, none or some or all of thetelematics-data-based inferences determined by the marketplace modulesbased on input data from a marketplace participant are shared with noneor some or all of the other marketplace participants of the plurality ofmarketplace participants.

In various embodiments, systems and methods of the present disclosureprovide a shared telematics data-based marketplace configured to collecttelematics data, from a plurality of vehicle operators, using aplurality of software applications including a common SDK and furtherconfigured to share, with a plurality of marketplace participants, thetelematics data and/or telematics-data-based inferences (e.g., score(s),rating(s), insight(s), recommendation(s), and/or calculation result(s))determined based at least in part upon the telematics data.

In various embodiments, systems and methods of the present disclosureprovide a telematics-data-based marketplace with a plurality of accesslevels. Each access level of the plurality of access levels may beassociated with a privacy level and/or a security level such that amarketplace participant granted with such access level is protectedagainst unwanted disclosure of certain telematics data and/or telematicsdata-based inferences. In some examples, a marketplace participant mayselect which access level it will allow, for part or all of itstelematics data and/or telematics data-based inferences, othermarketplace participants to access. For example, a marketplaceparticipant may select an access level that requires other marketplaceparticipants to acquire approval, such as via an authentication processor a transaction, before being allowed to access and/or use part of allof its telematics data available on the telematics-based marketplace. Asanother example, a marketplace participant may select a no-access accesslevel that forbids any third party from accessing and/or using itstelematics data available on the telematics-based marketplace. Suchno-access access level may be desirable for marketplace participantswhose clients or users chose to opt-out from data share or data sale.

In various embodiments, a marketplace participant is a marketplacesupplier when it supplies telematics data and/or telematics-data-basedinferences onto the marketplace. In various examples, a marketplaceparticipant is a marketplace consumer when it access telematics dataand/or telematics-data-based inferences available on the marketplace. Insome examples, a marketplace participant can be both a marketplacesupplier and a marketplace consumer. In certain examples, a marketplacesupplier may select an access level to control access of its telematicsdata and/or telematics-data-based inferences by marketplace consumer(s)who desire access. In certain examples, a marketplace consumer maypurchase an access level to gain access of a marketplace supplier'stelematics data and/or telematics-data-based inferences. In someexamples, a marketplace supplier may require a minimum access level fora marketplace consumer to have before allowing access to its telematicsdata and/or telematics-data-based inferences. In some examples, amarketplace supplier may set access restriction(s), such as accessrestriction(s) prohibiting marketplace consumer(s) of a particularindustry. In certain examples, access restriction may prohibitparticular competitors from access.

In various embodiments, systems and methods of the present disclosureprovide a telematics-based marketplace, collect and make availabletelematics data associated with a plurality of vehicle operators,optionally generate telematics-data-based inferences, present telematicsdata and/or telematics-data-based inferences to one or more marketplaceparticipants, and receive one or more requests for profile informationassociated with one or more interested vehicle operators of theplurality of vehicle operators based at least in part upon thetelematics data and/or the telematics-data-based inferences. In someexamples, the profile information requested are specific to one or moreadvertisements. In certain examples, the profile information arecollected manually and/or automatically from vehicle operator(s),marketplace participant(s), and/or public database(s). For example, themarketplace and/or the marketplace participants may collect personalinformation via vehicle operator-answered questionnaires,application-usage data, device-usage data, internet-browsing data,and/or government agencies. In some examples, a vehicle operator mayopt-out from permitting marketplace participants to upload and/or sharehis/her corresponding telematics data and/or personal data via themarketplace either in part or in full.

In various embodiments, systems and methods of the present disclosurecollect telematics data associated with a plurality of vehicle operatorscontinuously or intermittently such that the telematics data and/or theassociated telematics-data-based inferences presented via themarketplace to a plurality of marketplace participants are up to date.In some examples, the marketplace collects telematics data of a vehicleoperator before the vehicle operator becomes a user, customer, or clientof a marketplace participant, while the vehicle operator is a user,customer, or client of the marketplace participant, and/or after thevehicle operator has stopped being a user, customer, or client of themarketplace participant. In various examples, the plurality ofmarketplace participants may monitor a vehicle operator via thetelematics data and/or the associated telematics-data-based inferenceson the marketplace to determine whether to extend a new service ordiscount, and/or adjust an existing service or an associated price.

In various embodiments, systems and methods of the present disclosureprovide a telematics-based auction marketplace including one or moreauction features and/or mechanisms. For example, systems and methods ofthe present disclosure provide a telematics-based auction marketplaceconfigured to receive bids from a plurality of marketplace participantsfor one or more user profiles and the associated user information. Insome examples, systems and methods of the present disclosure presenttelematics data and/or telematics data-based inferences to a pluralityof marketplace participants such that the plurality of marketplaceparticipants may determine whether to bid for a user based at least inpart upon the user's associated telematics data and/or telematicsdata-based inferences. In certain examples, systems and methods of thepresent disclosure receive one or more bids from a plurality ofmarketplace participants automatically, continuously, and/orintermittently. For example, systems and methods of the presentdisclosure receive, such as via one or more software applications and/orhardware modules, new bid(s) and/or updated bid(s) for a vehicleoperator's information whenever new information becomes available on themarketplace.

In various embodiments, systems and methods of the present disclosureprovide a shared telematics-based marketplace and collect telematicsdata associated with a plurality of vehicle operators using one or moresoftware applications and/or hardware modules. In various examples, theone or more software applications and/or hardware modules may beconfigured to perform a primary task other than to collect telematicsdata. For example, the primary task may be to provide a userenvironmental information (e.g., weather), to provide a usergeolocational information and/or directions, to provide a userentertainment (e.g., gaming, music, movie, video), and/or to provide auser device information. In some examples, the one or more softwareapplications and/or hardware modules may be provided to a vehicleoperator by one or more marketplace participants. In certain examples,the one or more software applications and/or hardware modules maycollect telematics data in universal format(s) and/or protocol(s) forimproved interoperability.

In some examples, the one or more software applications and/or hardwaremodules may be provided by marketplace participants of the sameindustry. For example, systems and methods of the present disclosureprovide a shared telematics-based marketplace and collect a firsttelematics data associated with a first vehicle operator using a firstset of software applications and/or hardware modules, and collect asecond telematics data associated with a second vehicle operator using asecond set of software applications and/or hardware modules. The firstset of software applications and/or hardware modules may be provided bya first marketplace participant whom the first vehicle operator has arelationship with (e.g., as a client, a customer, and/or a user). Thesecond set of software applications and/or hardware modules may beprovided by a second marketplace participant whom the second vehicleoperator has a relationship with (e.g., as a client, a customer, and/ora user). Once collected, the marketplace may present the firsttelematics data, the second telematics data, and/or associatedtelematics data-based inference(s), to a plurality of marketplaceparticipants (e.g., including the first marketplace participant and/orthe second marketplace participant).

In various embodiments, systems and methods of the present disclosureprovide a telematics-based marketplace for user information to aplurality of marketplace participants. In some examples, thetelematics-based marketplace is provided as a universal portal orinterface for the plurality of marketplace participants to request,share, and/or analyze telematics data and/or telematics-data-basedinferences of one or more vehicle operators. For example, thetelematics-based marketplace collects and presents, such asautomatically, and/or continuously, telematics data and/ortelematics-data-based inferences from a plurality of sources includingone or more of the marketplace participants, public entities, and/ordirectly from devices associated with the vehicle operator(s).

In various embodiments, systems and methods of the present disclosuregenerate a score associated with each vehicle operator listed on thetelematics-data-based marketplace. In various examples, the score isgenerated based at least in part upon the associated vehicle operator'ssensor data. In some examples, the score is generated such that itrepresents or informs a risk of collision of the associated vehicleoperator. In certain examples, the score is generated such that itrepresents or informs a predicted cost and/or profitability should amarketplace participant acquire or maintain the vehicle operator as acustomer, user, or client. For example, the score may be a profitabilityscore and may be generated as a ratio of predicted cost to policypremium. In some examples, the predicted costs is generated based atleast in part upon the associated telematics data and/or additionalpersonal data (e.g., financial data, geolocational data, health data,activity data). In certain examples, multiple profitability scorescorresponding to multiple policy premium may be generated and presentedto a marketplace participant to help the marketplace participant todetermine which policy premium(s) would lead to a satisfactoryprofitability for the associated vehicle operator as a customer. Thismay help a marketplace participant to improve its pricing mechanismand/or profitability.

In various embodiments, systems and methods of the present disclosuregenerate, maintain, and update a score associated with each vehicleoperator listed on the telematics-data-based marketplace using aresidual model. In some examples, systems and methods of the presentdisclosure train and/or configure the residual model to determine aninitial score for each vehicle operator based at least in part upon thetelematics data collected prior to the listing of the vehicle operatoronto the marketplace. In various examples, systems and methods of thepresent disclosure train and/or configure the residual model to furtherdetermine an updated score based at least in part upon the initial scoreand newly collected telematics data collected during one or more recenttrips operated by the vehicle operator. In some examples, the residualmodel is trained, configured, maintained, and/or updated by one or moremarketplace participants and/or by one or more marketplacenon-participants, such as by a neutral entity providing thetelematics-data-based marketplace.

In various examples, a universal predictive model may be insulated frommarketplace participants such that formulas, weights, biases, andparameters are all determined and maintained by a neutral party and notby marketplace participant. Such universal predictive model may helpmaintain neutrality and avoid influence or control by the marketplaceparticipants. In some examples, a universal predictive model is opaqueto the marketplace participants such that the model functions similar toa black box in that the details of determinations and calculations arehidden to the marketplace participants. In various examples, the neutralparty, such as a marketplace administrator, may modify and maintain theuniversal model according to marketplace participants' needs, but suchchange may only be suggested and not required by the marketplaceparticipants to help maintain a fair marketplace. In some examples, thesystems and methods of the present disclosure identify one or moreuse-specific modifications, one or more market-specific modifications,one or more industry-specific modifications, and/or one or moreparticipant-specific modifications, such as based on user-acquisitiondata indicative of the success rates of user acquisition by marketplaceparticipants. A universal predictive model may also be referred to as aparty-neutral predictive model.

In various embodiments, systems and methods of the present disclosuregenerate one or more scores for each vehicle operator listed on thetelematics-data-based marketplace based at least in part upon comparingthe telematics data of the associated vehicle operator to the telematicsdata of one or more similar vehicle operators. In certain examples,systems and methods of the present disclosure train and/or configure theresidual model to determine the initial score and/or the updated scorebased not only upon the telematics data of an associated vehicleoperator, but also telematics data of a group of similar vehicleoperators. For example, the residual model may be trained or configuredto determine the initial score and/or the updated score as relativemetric(s) indicative of relative operation performance(s) and/orcharacteristic(s) of a first vehicle operator in comparison to a groupof similar vehicle operators. The relative metrics may be a ratio. As anexample, a vehicle operator may be scored relative to its group ofsimilar vehicle operators such that a predicted costs similar to that ofthe group of similar vehicle operators would result in a predictedrelative costs close to unity. The group of similar vehicle operatorsmay share similar geolocations, financial statuses, demographics,insurance providers, employers, and/or service providers. As an example,a first vehicle operator may drive similarly to a second vehicleoperator but scored a higher score because the groups of similar vehicleoperators to which the first and second vehicle operators are comparedagainst exhibit different levels of driving characteristics. In variousexamples, a vehicle operator may be associated with multiple groups ofsimilar vehicle operators and each relative metric may be determinedrelative to one or more of the multiple groups of similar vehicleoperators. In certain examples, systems and methods of the presentdisclosure may determine industry-specific, market-specific,use-specific, and/or relative metrics of a vehicle operator based on oneor more groups of similar vehicle operators to which the vehicleoperator is associated.

In some examples, systems and methods of the present disclosure maydetermine the score for a vehicle operator based at least in part uponthe associated telematics data and/or associated drivinghabits/patterns. For example, a better score may be determined for avehicle operator who drives riskier but less frequent than other similarvehicle operators. In some examples, a better score may be determinedfor a first vehicle operator who has had more insurance claims than asecond vehicle operator who has had no insurance claims, such as whenthe telematics data associated with the first vehicle operator indicateless risky driving behaviors when compared to the second vehicleoperator. In various examples, systems and methods of the presentdisclosure may determine the score for a vehicle operator based not onlyon claim history but also on sensor data that are indicative of thevehicle operator's driving behaviors.

In various examples, systems and methods of the present disclosure maydetermine a loss ratio as the score based at least in part upon avehicle operator's associated costs and policy premium paid throughoutthe policy term. In some examples, the loss ratio is presented to one ormore marketplace participants, such as including the insurance companythe vehicle operator has been insured with, to provide recommendation orguidance as to how to adjust pricing and/or policy to improveprofitability of insuring this particular vehicle operator and/orothers. As an example, a loss ratio that is less than 1 (e.g., 0.5) mayprompt a marketplace participant to issue a premium discount to avehicle operator, whereas a loss ratio that is more than 1 (e.g., 5) mayprompt the marketplace participant to increase the policy premium.

In certain examples, the residual model used to determine the loss ratiomay be trained to predict a predicted loss ratio. For example, systemsand methods of the present disclosure may train the residual model,which may be a machine learning model, using telematics data andhistoric loss ratios associated with a plurality of vehicle operators.The plurality of vehicle operators may have been insured by the same ordifferent insurance companies. Once trained, systems and methods of thepresent disclosure may determine, using the trained residual model, apredicted loss ratio for a vehicle operator based on the associatedtelematics data collected even when historic profitability data (e.g.,associated costs and policy premium paid over the past certain timeperiod) are unavailable. In some examples, systems and methods of thepresent disclosure determine the predicted profitability as a singlemetric to represent the desirability of a vehicle operator to amarketplace participant. The systems and methods of the presentdisclosure may configure and/or train the predictive model (e.g., aresidual model) for determining the predicted profitability to considertelematics data, claim history, costs, premium payments, and/oradditional user data.

In some examples, systems and methods of the present disclosure maydetermine an incremental loss ratio as the score based at least in partupon a vehicle operator's associated incremental costs and policypremium paid over the past certain time period (e.g., past season, pastmonth, past week, or past day). In certain examples, incremental costratio may be determined by dividing the incremental costs incurred bypremium payments received over the certain time period. In someexamples, the incremental loss ratio is presented to one or moremarketplace participants, such as including the insurance company thevehicle operator has been insured with, to provide recommendation orguidance as to how to adjust pricing and/or policy to improveprofitability of insuring this particular vehicle operator and/orothers. As an example, an incremental loss ratio that is less than 1(e.g., 0.5) may prompt a marketplace participant to issue a premiumdiscount to a vehicle operator, whereas an incremental loss ratio thatis more than 1 (e.g., 5) may prompt the marketplace participant toincrease the policy premium.

In certain examples, the residual model used to determine theincremental loss ratio may be trained to predict a predicted incrementalloss ratio. For example, systems and methods of the present disclosuremay train the residual model, which may be a machine learning model,using telematics data and historic incremental loss ratios associatedwith a plurality of vehicle operators. The plurality of vehicleoperators may have been insured by the same or different insurancecompanies. Once trained, systems and methods of the present disclosuremay determine, using the trained residual model, a predicted incrementalloss ratio for a vehicle operator based on the associated telematicsdata collected even when historic profitability data (e.g., associatedcosts and policy premium paid over the past certain time period) areunavailable. In some examples, systems and methods of the presentdisclosure determine the predicted profitability as a single metric torepresent the desirability of a vehicle operator to a marketplaceparticipant. The systems and methods of the present disclosure mayconfigure and/or train the predictive model (e.g., a residual model) fordetermining the predicted incremental profitability to considertelematics data, claim history, costs, premium payments, and/oradditional user data.

In various embodiments, systems and methods of the present disclosuredetermine and/or present, for each vehicle operator of a plurality ofoperators listed on a telematics-based marketplace, one or moreprice-adjusted metrics to a plurality of marketplace participants. Insome examples, the one or more price-adjusted metrics may providepricing guidance to the plurality of marketplace participants to achievea target profitability. In certain examples, the one or moreprice-adjusted metrics include a price-adjusted risk threshold, aprice-adjusted performance threshold, a price-adjusted mileagethreshold, and/or a price-adjusted cost threshold. In various examples,systems and methods of the present disclosure determine the one or moreprice-adjusted metrics based at least in part upon the telematics dataassociated with the plurality of operators. In certain examples, systemsand methods of the present disclosure determine the one or moreprice-adjusted metrics using one or more residual models configuredand/or trained for determining residual risk, performance threshold,mileage threshold, cost threshold, price-adjusted residual risk,price-adjusted performance threshold, price-adjusted mileage threshold,and/or price-adjusted cost threshold.

In various embodiments, systems and methods of the present disclosureconfigure and/or train one or more models to receive at least telematicsdata associated with a vehicle operator (e.g., collected via one or moresoftware applications and/or hardware modules) as input and to generatea predicted cost associated with the vehicle operator. In some examples,the predicted cost includes predicted losses and predicted expenses overa predicted policy term (e.g., the time during which the vehicleoperator is predicted to be a customer to a marketplace participant). Incertain examples, the predicted losses are associated with one or morepredicted claims that may occur during the predicted policy term. Insome examples, the predicted losses include payouts for vehicle repairs,replacements, property damage payouts, and/or personal injury payouts.In various examples, the predicted expenses include costs of customeracquisition, costs of customer retention, telematics data collectioncosts, telematics data processing costs, costs of operation associatedwith the telematics-based marketplace, and/or costs of customer service.In some examples, the one or more models includes a loss model fordetermining predicted losses and an expense model for determiningpredicted expenses.

In various embodiments, systems and methods of the present disclosureconfigure and/or train one or more models to receive at leastincremental telematics data (e.g., telematics data collected over acertain time period) associated with a vehicle operator (e.g., collectedvia one or more software applications and/or hardware modules) as inputand to generate an predicted incremental cost associated with thevehicle operator. In some examples, the predicted incremental costsincludes predicted incremental losses and predicted incremental expensesover a pre-determined time period (e.g., a fixed time period duringwhich the vehicle operator is predicted to be a customer to amarketplace participant). In certain examples, the predicted incrementallosses are associated with one or more predicted claims that may occurduring the pre-determined time period. In some examples, the predictedincremental losses include payouts for vehicle repairs, replacements,property damage payouts, and/or personal injury payouts. In variousexamples, the predicted incremental expenses include costs of customeracquisition, costs of customer retention, telematics data collectioncosts, telematics data processing costs, costs of operation associatedwith the telematics-based marketplace, and/or costs of customer service.In some examples, the one or more models includes an incremental lossmodel for determining predicted incremental losses and an incrementalexpense model for determining predicted incremental expenses.

In some examples, systems and methods of the present disclosure collect,for each vehicle operator listed on the telematics-based marketplace,incremental telematics data, incremental costs, incremental expenses,and/or incremental losses. In certain examples, as more costs and/orexpenses data are collected over an increasing length of periods of timefor a plurality of vehicle operators, systems and methods of the presentdisclosure may configure and/or train the one or more models to predictone or more predicted trends, such as one or more predicted trends ofone or more incremental metrics. In various examples, systems andmethods of the present disclosure determine, for each vehicle operatorlisted on the telematics-based marketplace, a predicted profitability,based at least in part upon the predicted costs, a predicted risk,and/or a predicted retention period (e.g., how long a vehicle operatoris predicted to remain a customer to a marketplace participant). In someexamples, systems and methods of the present disclosure determine apredicted value associated with each vehicle operator based on thepredicted profitability for an expected retention period.

In various embodiments, systems and methods of the present disclosuretrain and/or configure a predictive model for generating, based at leastin part upon incremental telematics data and/or incremental costs, apredicted value associated with each vehicle operator listed on thetelematics-based marketplace. In some examples, the predicted valuegenerated for and presented to each marketplace participant may be thesame or different. For example, the predicted value may be generatedusing an universal model (e.g., with a set biases, set weights, and/orformulas), where the same predicted value would be presented to theplurality of marketplace participants. In another example, the predictedvalue may be generated, for each marketplace participant, using adifferent party-specific model or a different use-specific model, wheredifferent predicted values may be presented to the plurality ofmarketplace participants.

In various embodiments, systems and methods of the present disclosuremay generate telematics inferences based at least in part upontelematics data using an universal predictive model, such as onegenerated and maintained by the marketplace administrators.

In various embodiments, systems and methods of the present disclosuretrack, monitor, and/or determine, for each vehicle operator of aplurality of vehicle operators, an incremental profitability, an overallprofitability, a predicted incremental profitability, and/or predictedoverall profitability. For example, systems and methods of the presentdisclosure collect, such as continually, continuously, on set timepoints or periods, in real-time or in near real-time, telematics dataand/or additional operator data (e.g., financial data, lifestyle data,social data, and/or online activity data) corresponding to one or morevehicle operators. The systems and methods of the present disclosurefurther generate, for each vehicle operator using one or more predictivemodels and the collected telematics data and/or additional operatordata, incremental profitability and/or overall profitability. Theincremental profitability may be an actual profitability and may bedetermined by subtracting value received over a time period by costsincurred during the time period and dividing the sum by the duration ofthe time period.

In various embodiments, systems and methods of the present disclosureprovide a feedback associated with one or more vehicle operators to oneor more marketplace participants. The one or more vehicle operators maybe customers, clients, and/or users (e.g., trial users, subscribers,standard users, premium users) of the one or more marketplaceparticipants. In some examples, systems and methods of the presentdisclosure may collect telematics data and/or additional operator data(e.g., financial data, lifestyle data, social data, and/or onlineactivity data) at least from the one or more marketplace participants.The systems and methods of the present disclosure may further generateone or more desirability indices indicative of desirability of the oneor more vehicle operators to the one or more marketplace participants.The one or more desirability indices may include an incrementalprofitability, an overall profitability, a predicted incrementalprofitability, and/or predicted overall profitability.

In some examples, systems and methods of the present disclosure maygenerate, such as based on telematics data available on thetelematics-data-based marketplace, an initial predicted profitabilitywhen a vehicle operator first become a user of a marketplaceparticipant. After becoming a user, systems and methods of the presentdisclosure may further generate a first subsequent profitability metricbased at least in part upon telematics data collected during a firsttime period. After the first time period, systems and methods of thepresent disclosure may further generate a second subsequentprofitability metric based at least in part upon telematics datacollected during a second time period. In various examples, systems andmethods of the present disclosure may determine, based on the subsequentprofitability metrics, a match feedback (e.g., a score and/or trend)indicative of whether the vehicle operator matches, outperforms, orunderperforms the initial predicted profitability.

In certain examples, for the initial time periods (e.g., days or weekssince a vehicle operator become a user of a marketplace participant),the match feedback is more likely to be “match” since telematics data inthe early periods should not deviate much from telematics data availableon the marketplace as they are more closely related. In contrast, forlater time periods (e.g., months or years since a vehicle operatorbecome a user of a marketplace participant), the match feedback is lesslikely to be “match” because the vehicle operator's operation habits mayhave evolved and would be manifested in a change in telematics data whencompared to the early periods. In various embodiments, systems andmethods of the present disclosure may present, such as continuallyand/or every time a new match feedback is generated, the match feedbackto the marketplace participant such that the marketplace participant mayevaluate whether the vehicle operator is and/or will be a desirable(e.g., profitable) user. In some examples, systems and methods of thepresent disclosure may train one or more predictive models forgenerating profitability metrics (e.g., including cost metrics) based atleast in part upon the match feedback.

In various embodiments, systems and methods of the present disclosuredetermine one or more user-management metrices indicative associatedwith a vehicle operator who's a user of a marketplace participant. Incertain examples, the one or more user-management metrices includeactual costs of acquisition, actual costs of risk events, actualcustomer service expenses, actual costs of retention, probability ofconversion at time of acquisition, probability of retention, probabilityof risk events, predicted costs of risk events, predicted customerservice expenses, and/or predicted costs of retention. In some examples,systems and methods of the present disclosure determine one or moreuser-management metrices for each vehicle operator of a plurality ofvehicle operators on the telematics-data-based marketplace and use saiddata to train one or more predictive models. In certain examples,systems and methods of the present disclosure may determine to themarketplace participant one or more predicted user-management metricesassociated with a prospective user of the marketplace participant. Insome examples, systems and methods of the present disclosure may presentthe one or more predicted user-management metrices to aid themarketplace participant to weigh the profitability of a vehicle operatoragainst the associated costs and likelihood of conversion. In certainexamples, systems and methods of the present disclosure may determine acost-benefit score associated with each vehicle operator to amarketplace participant who's looking to acquire a user. In variousexamples, systems and methods of the present disclosure determine aretention recommendation based at least in part upon expectedprofitability of an associated vehicle operator.

In various embodiments, systems and methods of the present disclosuredetermine and present, such as for each vehicle operator on thetelematics-data-based marketplace, a metric of expected profits and ametric of expected costs. In some examples, the metric of expectedprofits and metric of expected costs together are indicative of thepotential value of a potential user or of a current user to one or moremarketplace participant. In certain examples, the metric of expectedprofits and/or metric of expected costs are calculated individually foreach marketplace participant, such as using party-specific algorithmsand/or use-specific algorithms. In various examples, the systems andmethods of the present disclosure may provide guidance to marketplaceparticipants in modifying one or more predictive models for generatingprofitability and associated metrics, one or more models of useracquisition, one or more models of user retention, and/or one or moremethods of collecting data associated with vehicle operators.

In various embodiments, systems and methods of the present disclosurepresent the metric of expected profits and/or metric of expected coststo all marketplace participants or to those who have requested suchinformation. In some examples, systems and methods of the presentdisclosure present the metric of expected profits and/or metric ofexpected costs to only selected authenticated marketplace participantsfor the associated vehicle operators. In certain examples, the selectedauthenticated marketplace participants may be granted an access keyassociated with a selection of vehicle operators that enables access tosome or all of determined metrics of expected profits and/or metrics ofexpected costs. In various examples, such access key may be termed, maybe renewed, may be terminated, such as at the discretion of anoriginating marketplace participant (to whom the vehicle operator is auser of), the vehicle operator, a marketplace administrator, or athird-party. In certain examples, systems and methods of the presentdisclosure train a predictive model for generating a metric of expectedprofits for a prospective user based at least in part upon a pluralityof metrics of actual profits associated with a plurality of past orcurrent users. In certain examples, systems and methods of the presentdisclosure train a predictive model for generating a metric of expectedcosts for a prospective user based at least in part upon a plurality ofmetrics of actual costs associated with a plurality of past or currentusers. In some examples, the predictive models may include machinelearning models, such as neural networks or a combination of machinelearning models.

In various embodiments, systems and methods of the present disclosuregenerate and/or provide one or more marketplace scores and/or trendsusing one or more marketplace predictive models, such as based ontelematics data available on the marketplace. In some examples, the oneor more marketplace scores are exclusively available to the marketplaceparticipants via the telematics-data-based-marketplace. For example, theone or more marketplace predictive models are kept confidential to themarketplace and not make available to any marketplace participants suchthat it is shielded from any marketplace participant-directed changes.In various examples, the systems and methods of the present disclosuremay train and modify the one or more marketplace predictive models inview of the interests, goals, and/or requests of the marketplaceparticipants. In some examples, the systems and methods of the presentdisclosure provide the marketplace scores and/or trends as uniqueinsights to its marketplace participants who would not able to obtainwith an alternative method or channel.

In various embodiments, systems and methods of the present disclosureprovide an algorithm input interface to its marketplace participants forreceiving algorithms provided by each marketplace participant. Thealgorithms may include one or more use-specific algorithms and/orparty-specific algorithms for determining custom scores (e.g.,profitability score) associated with one or more vehicle operators ofinterests. In some examples, systems and methods of the presentdisclosure may execute the algorithms to determine, based on telematicsdata and/or marketplace scores available on the marketplace, the customscores for the associated marketplace participants. In various examples,the custom scores may be determined as composite scores, such ascomposite scores determined based solely on marketplace scores, such asusing party-specified weights and biases. In some examples, the customscores are indicative of the desirability of the associated vehicleoperator to the marketplace participant. In certain examples, algorithmsprovided by a first marketplace participant may be shared with aselected other marketplace participants or to be shared with no othermarketplace participants.

In various embodiments, systems and methods of the present disclosuredetermine and/or present one or more marketplace scores and, such asupon request, descriptions of the associated marketplace algorithms suchthat the meaning of the marketplace scores are conveyed to themarketplace participants. For example, the descriptions may generallydescribe what factors were considered in the determination of theassociated marketplace scores. In some examples, such marketplace scoresmay be referred to as transparent marketplace scores when descriptionsof the associated marketplace algorithms are available. In someexamples, the provision of algorithm descriptions help aid marketplaceparticipants' determination of which marketplace scores to use or howmuch each marketplace score should be weighed and/or biased in designingits algorithms for determining use-specific and/or party-specificscores. In other examples, some marketplace scores may be provided tomarketplace participants without any algorithm descriptions and may bereferred to as opaque marketplace scores. Examples of a transparentscore may include an acceleration score, a braking score, a focus score,a steering score, a financial reliability score, and a demographicscore. Examples of an opaque score may include an overall desirabilityscore, a predicted profitability score, a predicted retention score, apredicted risk score, and a predicted costs score.

In various embodiments, systems and methods of the present disclosuredetermine and provide program-evaluation metrics (e.g., predictedprobability of acquisition, actual acquisition data, acquisition costs,and/or vehicle operator desirability scores or trends) to a marketplaceparticipant, such as one or more user-acquisition programs themarketplace participant is experimenting (e.g., in an A-B test). Forexample, systems and methods of the present disclosure determine andprovide a first set of program-evaluation metrics associated with afirst plurality of vehicle operators subject to a first user-acquisitionprogram, and determine and provide a second set of program-evaluationmetrics associated with a second plurality of vehicle operators subjectto a second user-acquisition program. Systems and methods of the presentdisclosure may further compare the first set of program-evaluationmetrics against the second set of program-evaluation metrics andgenerate a comparison report for the marketplace participants toevaluate the effectiveness of its user-acquisition programs. Forexample, the effectiveness of one or more acquisition incentives, one ormore acquisition promotions, one or more acquisition discounts, and/orone or more acquisition services may be extrapolated from the comparisonreport.

As another example, systems and methods of the present disclosuredetermine and provide program-evaluation metrics (e.g., predictedprobability of acquisition, actual acquisition data, acquisition costs,and/or vehicle operator desirability scores or trends) to a marketplaceparticipant for one or more user-retention programs the marketplaceparticipant is experimenting (e.g., in an A-B test). For example,systems and methods of the present disclosure determine and provide afirst set of program-evaluation metrics associated with a firstplurality of vehicle operators subject to a first user-retentionprogram, and determine and provide a second set of program-evaluationmetrics associated with a second plurality of vehicle operators subjectto a second user-retention program. Systems and methods of the presentdisclosure may further compare the first set of program-evaluationmetrics against the second set of program-evaluation metrics andgenerate a comparison report for the marketplace participants toevaluate the effectiveness of its user-retention programs. For example,the effectiveness of one or more retention incentives, one or moreretention promotions, one or more retention discounts, and/or one ormore retention services may be extrapolated from the comparison report.

In various embodiments, systems and methods of the present disclosureprovide a telematics auction marketplace, provide telematics data,provide telematics-data-based inferences (e.g., marketplace scores,use-specific scores, party-specific scores), and receive a plurality ofbids from a plurality of marketplace participants. In some examples, theplurality of bids may be provided by the marketplace participants basedon the desirability of an associated vehicle operator. In variousexamples, the plurality of bids are indicative of the degree of interestthe marketplace participants have for an associated vehicle operator. Incertain examples, the plurality of bids may be at least for profileinformation of the associated vehicle operator, advertisementopportunity, advertisement priority, and/or information releasepriority. As an example, systems and methods of the present disclosuremay provide a marketplace participant of a winning bid additionalprofile information of a vehicle operator associated with the telematicsdata and/or inferences displayed. Systems and methods of the presentdisclosure may further provide the marketplace participant of thewinning bid with one or more advertisement opportunities via one or moreadvertisement channels on record showing user activity, such as via anapplication the user uses, via a webpage visited by the user, via a gameplayed by the user, and/or via a billboard positioned by a route theuser travels through.

In various embodiments, systems and methods of the present disclosureprovide a telematics auction marketplace configured to select aplurality of winning bids. In some examples, the telematics auctionmarketplace may auction information related to a vehicle operator to aplurality of marketplace participants, receive a plurality of bids froma plurality of marketplace participants, determine a number of winningbids satisfying a bid threshold (e.g., top percentile among the bids inbid amount and/or in bid time and/or satisfying a predetermined monetaryor time threshold), determine different priorities won by the number ofwinning bids, determine different winning packages associated with thedifferent priorities, and delivering the winning packages to a number ofwinning bidders associated with the number of winning bids. In variousexamples, systems and methods of the present disclosure determine anumber of winning bids by at least determining a first winning bid and asecond winning bid. The first winning bid, when compared to the secondwinning bid, may be an earlier bid and/or higher bid, or alternativelyhave the same bid time or at the same bid amount. In some examples,systems and methods of the present disclosure determine the differentpriorities by at least determining the first winning bid to have won afirst winning priority and the second winning bid to have won a secondwinning priority. The first winning priority may be higher or equal tothe second winning priority. In certain examples, systems and methods ofthe present disclosure determine the winning packages at leastdetermining a first winning package associated with the first winningpriority and a second winning package associated with the second winningpriority. The first winning package may include a first data packagerelated to a vehicle operator that is more valuable (e.g., more detailedand/or more advanced such as analytical inferences rather than raw data)than a second data package included in the second winning package. Thefirst winning package may alternatively include the same data package asthe second winning package but the first winning package is delivered tothe associated first winning bidder at an earlier time period than thesecond winning package to the associated second winning bidder.

In various examples, systems and methods of the present disclosureprovide a telematics auction marketplace configured to release severallevels of information related to a vehicle operator listed on theauction marketplace. In some examples, systems and methods of thepresent disclosure generate a plurality of bidding time windows for themarketplace participants to bid. As an example, winning bidder(s) of thewinning bid(s) of a first bidding time window may be awarded the highestlevel of information when compared to winning bidder(s) of the winningbid(s) of all subsequent bidding time window(s) which occur later thanthe first bidding time window. The highest level of information mayinclude a high level composite score indicative of the desirability ofthe vehicle operator (e.g., specific to a specific industry, specificmarket, and/or specific use), whereas a lower level of information, suchas those included in winning bids of the subsequent bidding timewindows, may include more primitive data and/or primitive scores such asraw telematics data and/or granular scores indicative of granularoperator characteristics.

In various embodiments, systems and methods of the present disclosureprovide a telematics auction marketplace with conditional bidding,provide telematics data, provide telematics-data-based inferences (e.g.,marketplace scores, use-specific scores, party-specific scores), andreceive a plurality of conditional bids from a plurality of marketplaceparticipants. In some examples, the plurality of conditional bids may beprovided by the marketplace participants based on the desirability of anassociated vehicle operator. In various examples, the plurality ofconditional bids are indicative of the degree of interest themarketplace participants have for an associated vehicle operator. Incertain examples, the plurality of conditional bids may be at least forprofile information of the associated vehicle operator, advertisementopportunity, advertisement priority, and/or information releasepriority. As an example, systems and methods of the present disclosuremay provide a marketplace participant of a winning conditional bidadditional profile information of a vehicle operator associated with thetelematics data and/or inferences displayed. Systems and methods of thepresent disclosure may further provide the marketplace participant ofthe winning conditional bid with one or more advertisement opportunitiesvia one or more advertisement channels on record showing user activity,such as via an application the user uses, via a webpage visited by theuser, via a game played by the user, and/or via a billboard positionedby a route the user travels through.

In various examples, payment by a winning bidder associated with aconditional bid to the telematics auction marketplace may not beprocessed or completed (e.g., withheld from completion) until a paymentcondition is satisfied. This may be in contrast to a non-conditional bidwhere payment is processed either when the bid was selected as a winneror when the award (e.g., user information) is granted. In some examples,the payment condition may be a predetermined time of retention, apredetermined profitability, a predetermined profits, a predeterminedrevenue, a conversion event, an acquisition event. As an example,systems and methods of the present disclosure may deliver an awardpackage (e.g., user information) to a marketplace participant associatedwith a winning bid without processing the transaction payment. Followingthe award, systems and methods of the present disclosure may monitor oneor more metrics associated with the vehicle operator and the winningmarketplace participant. Upon the fulfillment of the payment condition,such as upon the vehicle operator becoming a user of the marketplaceparticipant, the payment may be processed for the transaction.

In various examples, payment by a winning bidder associated with aconditional bid to the telematics auction marketplace may be processedpartially (e.g., half) at the time of award and processed partially(e.g., the other half) upon the fulfillment of the payment condition.The payment condition may be a user relationship milestone (e.g., yearsof being a user exceeding a threshold) or profitability milestone (e.g.,accumulative profits exceeding a threshold). In some examples, aplurality of payment conditions may be conditioned into a conditionalbid that, if the bid won, would enable a gradual payment structure withone of multiple payments (e.g., installments) processed upon fulfillmentof each of the plurality of payment conditions. As an example, systemsand methods of the present disclosure may process a first payment at thetime of delivering the award package, process a second payment at thetime of user acquisition, process a third payment at the time ofbreakeven (e.g., when user revenue exceeds costs of user acquisition orwhen the user-resulted revenue exceeds user-resulted costs), process afourth payment at the time of the minimum profitability (e.g., when theuser-resulted revenue exceeds user-resulted costs by a predeterminedthreshold), and/or process a fifth payment at the time of minimumrelationship time (e.g., when the vehicle operator remains a user to themarketplace participant beyond a predetermined threshold). In someexamples, a bidding fee related to entering of a marketplaceparticipant's bid may be processed before the bid is considered in theauctioning process. In certain examples, an access fee related toaccessing telematics data and/or telematics inferences may be processed.In certain examples, a subscription fee related to allowing usage of thetelematics marketplace may be processed.

In various embodiments, systems and methods of the present disclosureprovide a telematics auction marketplace configured to receiveconditional bids with multiple payment conditions. In some examples,systems and methods of the present disclosure train and/or implement oneor more bid profitability predictive models for determining thepredicted profit of a conditional bid. In certain examples, systems andmethods of the present disclosure determine, using the one or more bidprofitability predictive models, a predicted long-term profitability ofa conditional bid. In some examples, the predicted long-termprofitability of a conditional bid may be a complex profitability metricfactoring profitability during a plurality of time periods. For example,the predicted long-term profitability may include a firstsub-profitability corresponding to a time period after a firstsub-payment is processed after delivering the award package, a secondsub-profitability corresponding to a time period after a secondsub-payment is processed after user acquisition, a thirdsub-profitability corresponding to a time period after a thirdsub-payment is processed at breakeven (e.g., when user revenue exceedscosts of user acquisition or when the user-resulted revenue exceedsuser-resulted costs), a fourth sub-profitability corresponding to a timeperiod after a fourth sub-payment is processed at minimum profitability(e.g., when the user-resulted revenue exceeds user-resulted costs by apredetermined threshold), and/or a fifth sub-profitability correspondingto a time period after a fifth sub-payment is processed at the time ofminimum relationship time (e.g., when the vehicle operator remains auser to the marketplace participant beyond a predetermined threshold).

In various examples, systems and methods of the present disclosurecollect user management data including acquisition data, retention data,costs data, and/or revenue data. In some examples, systems and methodsof the present disclosure collect user management data of a plurality ofvehicle operators listed on the telematics marketplace, which may beusers, customers, and/or clients of one or more marketplaceparticipants. In various examples, systems and methods of the presentdisclosure train one or more bid profitability predictive models usingthe collected user management data. In certain examples, systems andmethods of the present disclosure determine, using the trained one ormore bid profitability predictive models and/or available data of avehicle operator and/or data of the marketplace participants, howprofitable any given marketplace participant would be if matched withthe vehicle operator. In some examples, such determination may includedetermining a predicted period of retention, a predicted costs if becomea user of the marketplace participant, and a predicted revenue ifbecoming a user of the marketplace participant. In certain examples,circumstantial values may be considered to determine whether a userwould be a good match with a marketplace participant. Suchcircumstantial values may include social values (e.g., environmentalstance).

In various embodiments, systems and methods of the present disclosureprovide an application or web-service to which a user may enroll in orsubscribe to. In some examples, the application or web-service maycollect user input data (e.g., user preferences), user data (e.g., usagecharacteristics), and/or telematics data from the user and/orthird-party sources. In various examples, the application or web-servicemay upload the collected data onto the telematics-data-based marketplacesuch that a plurality of marketplace participants may determine whetherto bid on the user. In certain examples, the application or web-servicemay receive or collect offers from marketplace participants for aplurality of products and automatically select a desired offer. Invarious examples, the application or web-service may automaticallymonitor new insurance policy offers extended to the user, such aswhenever new telematics data and/or user data are uploaded onto thetelematics marketplace. In some examples, application or web-service mayautomatically switch from existing insurance policy to one of the newinsurance policy offers, such as based on user preferences and/or usagecharacteristics.

Examples of Various Embodiments of the Present Disclosure

According to various embodiments, a computer-implemented method for datamanagement includes: collecting a plurality of personal data setsassociated with a plurality of vehicle operators continually; collectinga plurality of sensor data sets associated with the plurality of vehicleoperators continually via one or more sensing modules; for each vehicleoperator of the plurality of vehicle operators: generating andcontinually updating an operator profile including the personal data setassociated with the vehicle operator; determining and continuallyupdating one or more telematics inferences based at least in part uponthe sensor data set associated with the vehicle operator; generating andcontinually updating a data profile including the one or more telematicsinferences associated with the vehicle operator; and listing andcontinually updating the data profile onto a telematics marketplace tobe accessible by a plurality of marketplace participants; receiving,from a plurality of bidders of the plurality of marketplaceparticipants, a plurality of conditional bids for a target operatorprofile associated with a target data profile selected from the listeddata profiles of the plurality of vehicle operators, each conditionalbid of the plurality of conditional bids including one or moreconditional payments and one or more payment conditions; determining,for each conditional bid of the plurality of conditional bids, apredicted bid-generated profit or a predicted bid-generated revenue;determining, based at least in part upon the predicted profit orpredicted bid-generated revenue, a winning bid and an associated winningbidder; and transmitting the target operator profile to the winningbidder. In some examples, the method is implemented according to method300 of FIG. 3 , and/or configured to be implemented by system 100 ofFIG. 1 , system 200 of FIG. 2 , device 5000 of FIG. 4 , and/or system7000 of FIG. 5 .

In some embodiments, determining the winning bid includes determiningthe winning bid as the bid of the plurality of conditional bidders whichhas the highest predicted bid-generated profit for a marketplace entity.

In some embodiments, determining the predicted bid-generated profitincludes determining a predicted bid-generated revenue and a predictedbid-generated costs, and subtracting the predicted bid-generated revenueby the predicted bid-generated costs.

In some embodiments, determining the predicted bid-generated profitincludes determining a predicted user retention duration, anddetermining the winning bid as the bid of the plurality of conditionalbids which has the highest predicted long-term bid-generated profit forthe full duration of the predicted user pretention duration.

In some embodiments, determining the predicted bid-generated profitincludes determining a predicted user retention duration, anddetermining the winning bid as the bid of the plurality of conditionalbids which has the highest predicted period-specific bid-generatedprofit for a period of interest predetermined by the marketplace entity.

In some embodiments, determining the predicted bid-generated profitincludes determining, for each payment condition of the one or morepayment conditions, a likelihood of condition fulfillment.

In some embodiments, determining the predicted bid-generated profitincludes multiplying, for each payment condition of the one or morepayment conditions, the likelihood of condition fulfillment and anassociated conditional payment of the one or more conditional payments.

In some embodiments, the one or more conditional payments includes afirst conditional payment and a second conditional payment; the one ormore conditional payment conditions includes a first payment conditionand a second payment condition; the first conditional payment iswithheld from completion at least until the first payment condition issatisfied; and the second conditional payment is withheld fromcompletion at least until the first payment condition and the secondpayment condition are satisfied.

In some embodiments, the one or more sensing modules includes a commonmodule used by a plurality of mobile applications; the common module isa software module or a common hardware module; and each vehicle operatoruses at least one mobile application of the plurality of mobileapplications.

According to various embodiments, a computing system for one or moreprocessors; and a memory storing instructions that, upon execution bythe one or more processors, cause the computing system to perform one ormore processes including: collecting a plurality of personal data setsassociated with a plurality of vehicle operators continually; collectinga plurality of sensor data sets associated with the plurality of vehicleoperators continually via one or more sensing modules; for each vehicleoperator of the plurality of vehicle operators: generating andcontinually updating an operator profile including the personal data setassociated with the vehicle operator; determining and continuallyupdating one or more telematics inferences based at least in part uponthe sensor data set associated with the vehicle operator; generating andcontinually updating a data profile including the one or more telematicsinferences associated with the vehicle operator; and listing andcontinually updating the data profile onto a telematics marketplace tobe accessible by a plurality of marketplace participants; receiving,from a plurality of bidders of the plurality of marketplaceparticipants, a plurality of conditional bids for a target operatorprofile associated with a target data profile selected from the listeddata profiles of the plurality of vehicle operators, each conditionalbid of the plurality of conditional bids including one or moreconditional payments and one or more payment conditions; determining,for each conditional bid of the plurality of conditional bids, apredicted bid-generated profit or a predicted bid-generated revenue;determining, based at least in part upon the predicted profit orpredicted bid-generated revenue, a winning bid and an associated winningbidder; and transmitting the target operator profile to the winningbidder. In some examples, the system is configured accordingly to system100 of FIG. 1 , system 200 of FIG. 2 , device 5000 of FIG. 4 , and/orsystem 7000 of FIG. 5 and/or configured to perform method 300 of FIG. 3.

According to various embodiments, a non-transitory computer-readablemedium storing instructions for data management, the instructions uponexecution by one or more processors of a computing system, cause thecomputing system to perform one or more processes including: collectinga plurality of personal data sets associated with a plurality of vehicleoperators continually; collecting a plurality of sensor data setsassociated with the plurality of vehicle operators continually via oneor more sensing modules; for each vehicle operator of the plurality ofvehicle operators: generating and continually updating an operatorprofile including the personal data set associated with the vehicleoperator; determining and continually updating one or more telematicsinferences based at least in part upon the sensor data set associatedwith the vehicle operator; generating and continually updating a dataprofile including the one or more telematics inferences associated withthe vehicle operator; and listing and continually updating the dataprofile onto a telematics marketplace to be accessible by a plurality ofmarketplace participants; receiving, from a plurality of bidders of theplurality of marketplace participants, a plurality of conditional bidsfor a target operator profile associated with a target data profileselected from the listed data profiles of the plurality of vehicleoperators, each conditional bid of the plurality of conditional bidsincluding one or more conditional payments and one or more paymentconditions; determining, for each conditional bid of the plurality ofconditional bids, a predicted bid-generated profit or a predictedbid-generated revenue; determining, based at least in part upon thepredicted profit or predicted bid-generated revenue, a winning bid andan associated winning bidder; and transmitting the target operatorprofile to the winning bidder. In some examples, the non-transitorycomputer-readable medium, upon execution by one or more processorsassociated with system 100 of FIG. 1 , system 200 of FIG. 2 , device5000 of FIG. 4 , and/or system 7000 of FIG. 5 , causes the correspondingsystem to perform method 300 of FIG. 3 .

Examples of Some Embodiments of the Present Disclosure

In certain embodiments, systems and methods of the present disclosureprovide a marketplace where one or more profiles and/or user data of oneor more vehicle operators may be shared and/or requested, such as basedon telematics data associated with the one or more vehicle operators.

In certain embodiments, systems and methods of the present disclosureprovide a marketplace for sharing one or more vehicle operator profilesbased at least in part upon telematics data, such as raw sensor data. Invarious examples, a system (e.g., one including modules to perform amethod) and/or a method for sharing operator profiles via a marketplaceincludes: receiving sensor data associated with a plurality of vehicleoperators, the sensor data collected via one or more sensors associatedwith each vehicle operator of the plurality of vehicle operators;generating, for each vehicle operator of the plurality of vehicleoperators, an operator profile including personal information associatedwith each vehicle operator; generating a plurality of data profilescorresponding to the plurality of vehicle operators such that each dataprofile of the plurality of data profiles includes the sensor dataassociated with one vehicle operator of the plurality of vehicleoperators; listing the plurality of data profiles on a marketplaceconfigured to be accessed by a plurality of parties (e.g., insurancecompanies, car rental companies, vehicle manufacturing companies,autonomous driving firms, shared ride companies, housing firms, banks,government agencies, etc.); receiving, from a requesting party of theplurality of parties, an information request for a target operatorprofile associated with a target data profile of the plurality of dataprofiles; and/or delivering, in response to receiving the informationrequest, the target operator profile to the requesting party.

In certain embodiments, systems and methods of the present disclosureprovide a marketplace for sharing one or more vehicle operator profilesbased at least in part upon operator score (e.g., determined based ontelematics data). In various examples, a system (e.g., one includingmodules to perform a method) and/or a method for sharing operatorprofiles via a marketplace includes: receiving sensor data associatedwith a plurality of vehicle operators, the sensor data collected via oneor more sensors associated with each vehicle operator of the pluralityof vehicle operators; generating, for each vehicle operator of theplurality of vehicle operators, an operator profile including personalinformation associated with each vehicle operator; generating, for eachvehicle operator of the plurality of vehicle operators, one or moreoperator scores (e.g., safety score, reliability score, drivingcharacteristic scores such as acceleration, braking, cornering, and/orscore indicative of behavioral insights of the operator) based at leastin part upon the sensor data; generating a plurality of data profilescorresponding to the plurality of vehicle operators such that each dataprofile of the plurality of data profiles includes the one or moreoperator scores associated with one vehicle operator of the plurality ofvehicle operators; listing the plurality of data profiles on amarketplace configured to be accessed by a plurality of parties (e.g.,insurance companies, car rental companies, vehicle manufacturingcompanies, autonomous driving firms, shared ride companies, housingfirms, banks, government agencies, etc.); receiving, from a requestingparty of the plurality of parties, an information request for a targetoperator profile associated with a target data profile of the pluralityof data profiles; and/or delivering, in response to receiving theinformation request, the target operator profile to the requestingparty.

In certain embodiments, systems and methods of the present disclosureprovide a marketplace for sharing one or more vehicle operator profilesbased at least in part upon universal operator score (e.g., determinedbased on telematics data). In various examples, a system (e.g., oneincluding modules to perform a method) and/or a method for sharingoperator profiles via a marketplace includes: receiving sensor dataassociated with a plurality of vehicle operators, the sensor datacollected via one or more sensors associated with each vehicle operatorof the plurality of vehicle operators; generating, for each vehicleoperator of the plurality of vehicle operators, an operator profileincluding personal information associated with each vehicle operator;generating, for each vehicle operator of the plurality of vehicleoperators, one or more operator scores (e.g., safety score, reliabilityscore, driving characteristic scores such as acceleration, braking,cornering, and/or score indicative of behavioral insights of theoperator) based at least in part upon the sensor data; generating aplurality of data profiles corresponding to the plurality of vehicleoperators such that each data profile of the plurality of data profilesincludes the one or more operator scores associated with one vehicleoperator of the plurality of vehicle operators; listing the plurality ofdata profiles on a marketplace configured to be accessed by a pluralityof parties (e.g., insurance companies, car rental companies, vehiclemanufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.); receiving,from a requesting party of the plurality of parties, an informationrequest for a target operator profile associated with a target dataprofile of the plurality of data profiles; and/or delivering, inresponse to receiving the information request, the target operatorprofile to the requesting party. In some examples, generating the one ormore operator scores includes generating the one or more operator scoresusing a universal model configured to generate, such as based at leastin part upon sensor data, operator scores informative to a plurality ofuses associated with the plurality of third parties.

In certain embodiments, systems and methods of the present disclosureprovide a marketplace for sharing one or more vehicle operator profilesbased at least in part upon party-specific operator score (e.g.,determined based on telematics data) and/or use-specific operator score(e.g., determined based on telematics data). In various examples, asystem (e.g., one including modules to perform a method) and/or a methodfor sharing operator profiles via a marketplace includes: receivingsensor data associated with a plurality of vehicle operators, the sensordata collected via one or more sensors associated with each vehicleoperator of the plurality of vehicle operators; generating, for eachvehicle operator of the plurality of vehicle operators, an operatorprofile including personal information associated with each vehicleoperator; generating, for each vehicle operator of the plurality ofvehicle operators, one or more operator scores (e.g., safety score,reliability score, driving characteristic scores such as acceleration,braking, cornering, and/or score indicative of behavioral insights ofthe operator) based at least in part upon the sensor data; generating aplurality of data profiles corresponding to the plurality of vehicleoperators such that each data profile of the plurality of data profilesincludes the one or more operator scores associated with one vehicleoperator of the plurality of vehicle operators; listing the plurality ofdata profiles on a marketplace configured to be accessed by a pluralityof parties (e.g., insurance companies, car rental companies, vehiclemanufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.); receiving,from a requesting party of the plurality of parties, an informationrequest for a target operator profile associated with a target dataprofile of the plurality of data profiles; and/or delivering, inresponse to receiving the information request, the target operatorprofile to the requesting party. In some examples, generating the one ormore operator scores includes: receiving, from the plurality of parties,a plurality of party-provided scoring models, each party-providedscoring model of the plurality of party-provided scoring models beingone of a use-specific model and a party-specific model and configured togenerate operator scores informative to at least one of a particular useand a particular party; selecting a party-provided scoring model of theplurality of party-provided scoring models based at least in part uponparty information; and/or generating the one or more operator scoresusing the selected party-provided scoring model based at least in partupon the sensor data.

In certain embodiments, systems and methods of the present disclosureprovide a marketplace with one or more security measures for sharing oneor more vehicle operator profiles based at least in part uponparty-specific operator score (e.g., determined based on telematicsdata) and/or use-specific operator score (e.g., determined based ontelematics data). In various examples, a system (e.g., one includingmodules to perform a method) and/or a method for sharing operatorprofiles via a marketplace includes: receiving sensor data associatedwith a plurality of vehicle operators, the sensor data collected via oneor more sensors associated with each vehicle operator of the pluralityof vehicle operators; generating, for each vehicle operator of theplurality of vehicle operators, an operator profile including personalinformation associated with each vehicle operator; generating, for eachvehicle operator of the plurality of vehicle operators, one or moreoperator scores (e.g., safety score, reliability score, drivingcharacteristic scores such as acceleration, braking, cornering, and/orscore indicative of behavioral insights of the operator) based at leastin part upon the sensor data; generating a plurality of data profilescorresponding to the plurality of vehicle operators such that each dataprofile of the plurality of data profiles includes the one or moreoperator scores associated with one vehicle operator of the plurality ofvehicle operators; listing the plurality of data profiles on amarketplace configured to be accessed by a plurality of parties (e.g.,insurance companies, car rental companies, vehicle manufacturingcompanies, autonomous driving firms, shared ride companies, housingfirms, banks, government agencies, etc.); receiving, from a requestingparty of the plurality of parties, an information request for a targetoperator profile associated with a target data profile of the pluralityof data profiles; and/or delivering, in response to receiving theinformation request, the target operator profile to the requestingparty. In some examples, generating the one or more operator scoresincludes: receiving, from the plurality of parties, a plurality ofparty-provided scoring models, each party-provided scoring model of theplurality of party-provided scoring models being one of a use-specificmodel and a party-specific model and configured to generate operatorscores informative to at least one of a particular use and a particularparty; imposing security measures including: limiting the plurality ofparty-provided scoring models to read-only (or use-only); verifying aparty-provided audit key for each party-provided scoring model; and/orgenerating, for each party-provided scoring model, a log recording eachmodel execution, the log being visible to the party who provided theparty-provided scoring model; selecting a party-provided scoring modelof the plurality of party-provided scoring models based at least in partupon party information; and/or generating the one or more operatorscores using the selected party-provided scoring model based at least inpart upon the sensor data.

In certain embodiments, systems and methods of the present disclosureprovide a marketplace for sharing one or more vehicle operator profilesbased at least in part upon predicted party-specific operator score(e.g., determined based on telematics data) and/or predicteduse-specific operator score (e.g., determined based on telematics data),such as using one or more machine learning models. In various examples,a system (e.g., one including modules to perform a method) and/or amethod for sharing operator profiles via a marketplace includes:receiving sensor data associated with a plurality of vehicle operators,the sensor data collected via one or more sensors associated with eachvehicle operator of the plurality of vehicle operators; generating, foreach vehicle operator of the plurality of vehicle operators, an operatorprofile including personal information associated with each vehicleoperator; generating, for each vehicle operator of the plurality ofvehicle operators, one or more operator scores (e.g., safety score,reliability score, driving characteristic scores such as acceleration,braking, cornering, and/or score indicative of behavioral insights ofthe operator) based at least in part upon the sensor data; generating aplurality of data profiles corresponding to the plurality of vehicleoperators such that each data profile of the plurality of data profilesincludes the one or more operator scores associated with one vehicleoperator of the plurality of vehicle operators; listing the plurality ofdata profiles on a marketplace configured to be accessed by a pluralityof parties (e.g., insurance companies, car rental companies, vehiclemanufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.); receiving,from a requesting party of the plurality of parties, an informationrequest for a target operator profile associated with a target dataprofile of the plurality of data profiles; and/or delivering, inresponse to receiving the information request, the target operatorprofile to the requesting party. In some examples, generating the one ormore operator scores includes: training a plurality of score-predictingmodels trained to generate, given the same input parameters, operatorscores similar to a plurality of party-owned scoring models associatedwith the plurality of parties; selecting a score-predicting model of theplurality of score-predicting models based at least in part upon partyinformation; and/or generating the one or more operator scores using theselected party-provided scoring model based at least in part upon thesensor data. In some examples, systems and/or methods for training aprediction model (e.g., an artificial intelligence-based model) may beprovided, such as for training the prediction model based at least inpart upon taking sensor data input and output scores from parties' ownmodels.

In certain embodiments, systems and methods of the present disclosureprovide a marketplace for sharing one or more vehicle operator profilesbased at least in part upon operator score and/or one or moresub-scores. In various examples, a system (e.g., one including modulesto perform a method) and/or a method for sharing operator profiles via amarketplace includes: receiving sensor data associated with a pluralityof vehicle operators, the sensor data collected via one or more sensorsassociated with each vehicle operator of the plurality of vehicleoperators; generating, for each vehicle operator of the plurality ofvehicle operators, an operator profile including personal informationassociated with each vehicle operator; generating, for each vehicleoperator of the plurality of vehicle operators, a single operator scorebased at least in part upon the sensor data; generating a plurality ofdata profiles corresponding to the plurality of vehicle operators suchthat each data profile of the plurality of data profiles includes thesingle operator score associated with one vehicle operator of theplurality of vehicle operators; listing the plurality of data profileson a marketplace configured to be accessed by a plurality of parties(e.g., insurance companies, car rental companies, vehicle manufacturingcompanies, autonomous driving firms, shared ride companies, housingfirms, banks, government agencies, etc.); receiving, from a requestingparty of the plurality of parties, insight request for a target operatorprofile associated with a target data profile of the plurality of dataprofiles; generating, in response to receiving the insight request, oneor more insight scores (e.g., safety score, reliability score, drivingcharacteristic scores such as acceleration, braking, cornering, rawsensor data, and/or score indicative of behavioral insights of theoperator) associated with the target operator profile; receiving, fromthe requesting party, an information request for a target operatorprofile associated with a target data profile of the plurality of dataprofiles; and/or delivering, in response to receiving the informationrequest, the target operator profile to the requesting party.

In certain embodiments, systems and methods of the present disclosureprovide a marketplace for sharing one or more vehicle operator profilesbased at least in part upon tiers of operator scores (e.g., of theassociated vehicle operators). In various examples, a system (e.g., oneincluding modules to perform a method) and/or a method for sharingoperator profiles via a marketplace includes: receiving sensor dataassociated with a plurality of vehicle operators, the sensor datacollected via one or more sensors associated with each vehicle operatorof the plurality of vehicle operators; generating, for each vehicleoperator of the plurality of vehicle operators, an operator profileincluding personal information associated with each vehicle operator;generating, for each vehicle operator of the plurality of vehicleoperators, a single operator score based at least in part upon thesensor data; generating a plurality of data profiles corresponding tothe plurality of vehicle operators such that each data profile of theplurality of data profiles includes the single operator score associatedwith one vehicle operator of the plurality of vehicle operators;distributing the plurality of data profiles into a plurality of scoretiers based on the single operator score associated with each vehicleoperator of the plurality of vehicle operators; listing the plurality ofdata profiles on a marketplace configured to be accessed by a pluralityof parties (e.g., insurance companies, car rental companies, vehiclemanufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.) according tothe plurality of score tiers; receiving, from the requesting party, aninformation request for one or more target operator profiles of a targetscore tier of the plurality of score tiers; and/or delivering, inresponse to receiving the information request, one or more targetoperator profiles from the target score tier.

In certain embodiments, systems and methods of the present disclosureprovide an auction marketplace for sharing one or more vehicle operatorprofiles based at least in part upon telematics data, such as raw sensordata. In various examples, a system (e.g., one including modules toperform a method) and/or a method for sharing operator profiles via anauction marketplace includes: receiving sensor data associated with aplurality of vehicle operators, the sensor data collected via one ormore sensors associated with each vehicle operator of the plurality ofvehicle operators; generating, for each vehicle operator of theplurality of vehicle operators, an operator profile including personalinformation associated with each vehicle operator; generating aplurality of data profiles corresponding to the plurality of vehicleoperators such that each data profile of the plurality of data profilesincludes the sensor data associated with one vehicle operator of theplurality of vehicle operators; listing the plurality of data profileson an auction marketplace configured to be accessed by a plurality ofparties (e.g., insurance companies, car rental companies, vehiclemanufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.); receiving,from a plurality of requesting parties of the plurality of parties, aplurality of information requests for a target operator profileassociated with a target data profile of the plurality of data profiles;receiving a plurality of bids associated with the plurality orinformation requests; determining one or more winning bids from theplurality of bids; and/or delivering the target operator profile to oneor more winning parties associated with the one or more winning bids.

In certain embodiments, systems and methods of the present disclosureprovide an auction marketplace for sharing one or more vehicle operatorprofiles based at least in part upon operator score (e.g., determinedbased on telematic data of the associated vehicle operators). In variousexamples, a system (e.g., one including modules to perform a method)and/or a method for sharing operator profiles via an auction marketplaceincludes: receiving sensor data associated with a plurality of vehicleoperators, the sensor data collected via one or more sensors associatedwith each vehicle operator of the plurality of vehicle operators;generating, for each vehicle operator of the plurality of vehicleoperators, an operator profile including personal information associatedwith each vehicle operator; generating, for each vehicle operator of theplurality of vehicle operators, one or more operator scores (e.g.,safety score, reliability score, driving characteristic scores such asacceleration, braking, cornering, and/or score indicative of behavioralinsights of the operator) based at least in part upon the sensor data;generating a plurality of data profiles corresponding to the pluralityof vehicle operators such that each data profile of the plurality ofdata profiles includes the one or more operator scores associated withone vehicle operator of the plurality of vehicle operators; listing theplurality of data profiles on an auction marketplace configured to beaccessed by a plurality of parties (e.g., insurance companies, carrental companies, vehicle manufacturing companies, autonomous drivingfirms, shared ride companies, housing firms, banks, government agencies,etc.); receiving, from a plurality of requesting parties of theplurality of parties, a plurality of information requests for a targetoperator profile associated with a target data profile of the pluralityof data profiles; receiving a plurality of bids associated with theplurality or information requests; determining one or more winning bidsfrom the plurality of bids; and/or delivering the target operatorprofile to one or more winning parties associated with the one or morewinning bids.

In certain embodiments, systems and methods of the present disclosureprovide an auction marketplace for sharing one or more vehicle operatorprofiles based at least in part upon universal operator score (e.g.,determined based on telematic data of the associated vehicle operators).In various examples, a system (e.g., one including modules to perform amethod) and/or a method for sharing operator profiles via an auctionmarketplace includes: receiving sensor data associated with a pluralityof vehicle operators, the sensor data collected via one or more sensorsassociated with each vehicle operator of the plurality of vehicleoperators; generating, for each vehicle operator of the plurality ofvehicle operators, an operator profile including personal informationassociated with each vehicle operator; generating, for each vehicleoperator of the plurality of vehicle operators, one or more operatorscores (e.g., safety score, reliability score, driving characteristicscores such as acceleration, braking, cornering, and/or score indicativeof behavioral insights of the operator) based at least in part upon thesensor data; generating a plurality of data profiles corresponding tothe plurality of vehicle operators such that each data profile of theplurality of data profiles includes the one or more operator scoresassociated with one vehicle operator of the plurality of vehicleoperators; listing the plurality of data profiles on an auctionmarketplace configured to be accessed by a plurality of parties (e.g.,insurance companies, car rental companies, vehicle manufacturingcompanies, autonomous driving firms, shared ride companies, housingfirms, banks, government agencies, etc.); receiving, from a plurality ofrequesting parties of the plurality of parties, a plurality ofinformation requests for a target operator profile associated with atarget data profile of the plurality of data profiles; receiving aplurality of bids associated with the plurality or information requests;determining one or more winning bids from the plurality of bids; and/ordelivering the target operator profile to one or more winning partiesassociated with the one or more winning bids. In some examples,generating the one or more operator scores includes generating the oneor more operator scores using a universal model configured to generate,based at least in part upon sensor data, operator scores informative toa plurality of uses associated with the plurality of third parties.

In certain embodiments, systems and methods of the present disclosureprovide an auction marketplace for sharing one or more vehicle operatorprofiles based at least in part upon party-specific operator score(e.g., determined based on telematic data) and/or use-specific operatorscore (e.g., determined based on telematic data). In various examples, asystem (e.g., one including modules to perform a method) and/or a methodfor sharing operator profiles via an auction marketplace includes:receiving sensor data associated with a plurality of vehicle operators,the sensor data collected via one or more sensors associated with eachvehicle operator of the plurality of vehicle operators; generating, foreach vehicle operator of the plurality of vehicle operators, an operatorprofile including personal information associated with each vehicleoperator; generating, for each vehicle operator of the plurality ofvehicle operators, one or more operator scores (e.g., safety score,reliability score, driving characteristic scores such as acceleration,braking, cornering, and/or score indicative of behavioral insights ofthe operator) based at least in part upon the sensor data; generating aplurality of data profiles corresponding to the plurality of vehicleoperators such that each data profile of the plurality of data profilesincludes the one or more operator scores associated with one vehicleoperator of the plurality of vehicle operators; listing the plurality ofdata profiles on an auction marketplace configured to be accessed by aplurality of parties (e.g., insurance companies, car rental companies,vehicle manufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.); receiving,from a plurality of requesting parties of the plurality of parties, aplurality of information requests for a target operator profileassociated with a target data profile of the plurality of data profiles;receiving a plurality of bids associated with the plurality orinformation requests; determining one or more winning bids from theplurality of bids; and/or delivering the target operator profile to oneor more winning parties associated with the one or more winning bids. Insome examples, generating the one or more operator scores includes:receiving, from the plurality of parties, a plurality of party-providedscoring models, each party-provided scoring model of the plurality ofparty-provided scoring models being one of a use-specific model and aparty-specific model and configured to generate operator scoresinformative to at least one of a particular use and a particular party;selecting a party-provided scoring model of the plurality ofparty-provided scoring models based at least in part upon partyinformation; and/or generating the one or more operator scores using theselected party-provided scoring model based at least in part upon thesensor data.

In certain embodiments, systems and methods of the present disclosureprovide an auction marketplace with one or more security measures forsharing one or more vehicle operator profiles based at least in partupon party-specific operator score (e.g., determined based on telematicdata) and/or use-specific operator score (e.g., determined based ontelematic data). In various examples, a system (e.g., one includingmodules to perform a method) and/or a method for sharing operatorprofiles via an auction marketplace includes: receiving sensor dataassociated with a plurality of vehicle operators, the sensor datacollected via one or more sensors associated with each vehicle operatorof the plurality of vehicle operators; generating, for each vehicleoperator of the plurality of vehicle operators, an operator profileincluding personal information associated with each vehicle operator;generating, for each vehicle operator of the plurality of vehicleoperators, one or more operator scores (e.g., safety score, reliabilityscore, driving characteristic scores such as acceleration, braking,cornering, and/or score indicative of behavioral insights of theoperator) based at least in part upon the sensor data; generating aplurality of data profiles corresponding to the plurality of vehicleoperators such that each data profile of the plurality of data profilesincludes the one or more operator scores associated with one vehicleoperator of the plurality of vehicle operators; listing the plurality ofdata profiles on an auction marketplace configured to be accessed by aplurality of parties (e.g., insurance companies, car rental companies,vehicle manufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.); receiving,from a plurality of requesting parties of the plurality of parties, aplurality of information requests for a target operator profileassociated with a target data profile of the plurality of data profiles;receiving a plurality of bids associated with the plurality orinformation requests; determining one or more winning bids from theplurality of bids; and delivering the target operator profile to one ormore winning parties associated with the one or more winning bids. Insome examples, generating the one or more operator scores includes:receiving, from the plurality of parties, a plurality of party-providedscoring models, each party-provided scoring model of the plurality ofparty-provided scoring models being one of a use-specific model and aparty-specific model and configured to generate operator scoresinformative to a particular use and a particular party; imposingsecurity measures including one of: limiting the plurality ofparty-provided scoring models to read-only (or use-only); verifying aparty-provided audit key for each party-provided scoring model; and/orgenerating, for each party-provided scoring model, a log recording eachmodel execution, the log being visible to the party who provided theparty-provided scoring model; selecting a party-provided scoring modelof the plurality of party-provided scoring models based at least in partupon party information; and/or generating the one or more operatorscores using the selected party-provided scoring model based at least inpart upon the sensor data.

In certain embodiments, systems and methods of the present disclosureprovide an auction marketplace for sharing one or more vehicle operatorprofiles based at least in part upon party-specific operator score(e.g., determined based on telematic data) and/or use-specific operatorscore (e.g., determined based on telematic data) via one or more machinelearning algorithms. In various examples, a system (e.g., one includingmodules to perform a method) and/or a method for sharing operatorprofiles via an auction marketplace includes: receiving sensor dataassociated with a plurality of vehicle operators, the sensor datacollected via one or more sensors associated with each vehicle operatorof the plurality of vehicle operators; generating, for each vehicleoperator of the plurality of vehicle operators, an operator profileincluding personal information associated with each vehicle operator;generating, for each vehicle operator of the plurality of vehicleoperators, one or more operator scores (e.g., safety score, reliabilityscore, driving characteristic scores such as acceleration, braking,cornering, and/or score indicative of behavioral insights of theoperator) based at least in part upon the sensor data; generating aplurality of data profiles corresponding to the plurality of vehicleoperators such that each data profile of the plurality of data profilesincludes the one or more operator scores associated with one vehicleoperator of the plurality of vehicle operators; listing the plurality ofdata profiles on an auction marketplace configured to be accessed by aplurality of parties (e.g., insurance companies, car rental companies,vehicle manufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.); receiving,from a plurality of requesting parties of the plurality of parties, aplurality of information requests for a target operator profileassociated with a target data profile of the plurality of data profiles;receiving a plurality of bids associated with the plurality orinformation requests; determining one or more winning bids from theplurality of bids; and/or delivering the target operator profile to oneor more winning parties associated with the one or more winning bids. Insome examples, generating the one or more operator scores includes:training a plurality of score-predicting models trained to generate,given the same input parameters, operator scores similar to a pluralityof party-owned scoring models associated with the plurality of parties;selecting a score-predicting model of the plurality of score-predictingmodels based at least in part upon party information; and/or generatingthe one or more operator scores using the selected party-providedscoring model based at least in part upon the sensor data. In someexamples, systems and/or methods for training a prediction model (e.g.,an artificial intelligence-based model) may be provided, such as fortraining the prediction model based at least in part upon taking sensordata input and output scores from parties' own models.

In certain embodiments, systems and methods of the present disclosureprovide an auction marketplace for sharing one or more vehicle operatorprofiles based at least in part upon operator score and/or one or moresub-scores (e.g., determined based on telematic data). In variousexamples, a system (e.g., one including modules to perform a method)and/or a method for sharing operator profiles via an auction marketplaceincludes: receiving sensor data associated with a plurality of vehicleoperators, the sensor data collected via one or more sensors associatedwith each vehicle operator of the plurality of vehicle operators;generating, for each vehicle operator of the plurality of vehicleoperators, an operator profile including personal information associatedwith each vehicle operator; generating, for each vehicle operator of theplurality of vehicle operators, a single operator score based at leastin part upon the sensor data; generating a plurality of data profilescorresponding to the plurality of vehicle operators such that each dataprofile of the plurality of data profiles includes the single operatorscore associated with one vehicle operator of the plurality of vehicleoperators; listing the plurality of data profiles on an auctionmarketplace configured to be accessed by a plurality of parties (e.g.,insurance companies, car rental companies, vehicle manufacturingcompanies, autonomous driving firms, shared ride companies, housingfirms, banks, government agencies, etc.); receiving, from a requestingparty of the plurality of parties, insight request for a target operatorprofile associated with a target data profile of the plurality of dataprofiles; generating, in response to receiving the insight request, oneor more insight scores (e.g., safety score, reliability score, drivingcharacteristic scores such as acceleration, braking, cornering, rawsensor data, and/or score indicative of behavioral insights of theoperator) associated with the target operator profile; receiving, from aplurality of requesting parties of the plurality of parties, a pluralityof information requests for a target operator profile associated with atarget data profile of the plurality of data profiles; receiving aplurality of bids associated with the plurality or information requests;determining one or more winning bids from the plurality of bids; and/ordelivering the target operator profile to one or more winning partiesassociated with the one or more winning bids.

In certain embodiments, systems and methods of the present disclosureprovide an auction marketplace for sharing one or more vehicle operatorprofiles based at least in part upon tiers of operator score (e.g.,determined based on telematic data). In various examples, a system(e.g., one including modules to perform a method) and/or a method forsharing operator profiles via an auction marketplace includes: receivingsensor data associated with a plurality of vehicle operators, the sensordata collected via one or more sensors associated with each vehicleoperator of the plurality of vehicle operators; generating, for eachvehicle operator of the plurality of vehicle operators, an operatorprofile including personal information associated with each vehicleoperator; generating, for each vehicle operator of the plurality ofvehicle operators, a single operator score based at least in part uponthe sensor data; generating a plurality of data profiles correspondingto the plurality of vehicle operators such that each data profile of theplurality of data profiles includes the single operator score associatedwith one vehicle operator of the plurality of vehicle operators;distributing the plurality of data profiles into a plurality of scoretiers based on the single operator score associated with each vehicleoperator of the plurality of vehicle operators; listing the plurality ofdata profiles on an auction marketplace configured to be accessed by aplurality of parties (e.g., insurance companies, car rental companies,vehicle manufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.) according tothe plurality of score tiers; receiving, from a plurality of requestingparties of the plurality of parties, a plurality of information requestsfor a target operator profile associated with a target data profile ofthe plurality of data profiles; receiving a plurality of bids associatedwith the plurality or information requests; determining one or morewinning bids from the plurality of bids; and/or delivering the targetoperator profile to one or more winning parties associated with the oneor more winning bids.

In certain embodiments, systems and methods of the present disclosureprovide an auction marketplace for sharing one or more vehicle operatorprofiles based at least in part upon operator score (e.g., determinedbased on telematic data) using time-division auctioning. In variousexamples, a system (e.g., one including modules to perform a method)and/or a method for sharing operator profiles via an auction marketplaceincludes: receiving sensor data associated with a plurality of vehicleoperators, the sensor data collected via one or more sensors associatedwith each vehicle operator of the plurality of vehicle operators;generating, for each vehicle operator of the plurality of vehicleoperators, an operator profile including personal information associatedwith each vehicle operator; generating, for each vehicle operator of theplurality of vehicle operators, one or more operator scores (e.g.,safety score, reliability score, driving characteristic scores such asacceleration, braking, cornering, and/or score indicative of behavioralinsights of the operator) based at least in part upon the sensor data;generating a plurality of data profiles corresponding to the pluralityof vehicle operators such that each data profile of the plurality ofdata profiles includes the one or more operator scores associated withone vehicle operator of the plurality of vehicle operators; listing theplurality of data profiles on an auction marketplace configured to beaccessed by a plurality of parties (e.g., insurance companies, carrental companies, vehicle manufacturing companies, autonomous drivingfirms, shared ride companies, housing firms, banks, government agencies,etc.); receiving, from a plurality of requesting parties of theplurality of parties, a plurality of information requests for a targetoperator profile associated with a target data profile of the pluralityof data profiles; receiving a plurality of bids associated with theplurality or information requests; determining a highest winning bidfrom the plurality of bids; determining a second-highest winning bidfrom the plurality of bids; delivering the target operator profile to afirst winning party associated with the highest winning bid during afirst time period; and/or delivering the target operator profile to asecond winning party associated with the second-highest winning bidduring a second time period following the first time period.

In certain embodiments, systems and methods of the present disclosuremonitors one or more changes of one or more driving behaviors of one ormore vehicle operators as a function of business models. In variousexamples, a system (e.g., one including modules to perform a method)and/or a method for monitoring operator scores includes: receiving firstsensor data associated with a vehicle operator, the first sensor datacollected via one or more sensors associated with the vehicle operatorduring a first period corresponding to when the vehicle operator issubject to a first insurance model; generating one or more firstoperator scores (e.g., safety score, reliability score, drivingcharacteristic scores such as acceleration, braking, cornering, and/orscore indicative of behavioral insights of the operator) associated withthe vehicle operator based at least in part upon the first sensor data;receiving second sensor data associated with a vehicle operator, thesecond sensor data collected via the one or more sensors associated withthe vehicle operator during a second period corresponding to when thevehicle operator is subject to a second insurance model; generating oneor more second operator scores (e.g., safety score, reliability score,driving characteristic scores such as acceleration, braking, cornering,and/or score indicative of behavioral insights of the operator)associated with the vehicle operator based at least in part upon thesecond sensor data; and/or determining a score trend based at least inpart upon the one or more first operator scores and the one or moresecond operator scores, the score trend indicative of the effectivenessof an insurance model in encouraging safe driving behaviors.

In certain embodiments, systems and methods of the present disclosureshares one or more user profiles containing universal telematics datacollected via a shared module (e.g., a common software development kit).In various examples, a system (e.g., one including modules to perform amethod) and/or a method for sharing user profiles includes: establishinga marketplace accessible to a plurality of parties (e.g., insurancecompanies, car rental companies, vehicle manufacturing companies,autonomous driving firms, shared ride companies, housing firms, banks,government agencies, etc.); receiving telematics data from a pluralityof contributing parties of the plurality of parties, telematics databeing collected, by each contributing party of the plurality ofcontributing parties, via a shared module installed in at least one of aparty-associated hardware collector and a party-associatedprocessor-executable program (e.g., a gaming app, vehicle conditiontracking app, carbon emission monitoring app, on-board vehicle computerOS); generating a plurality of data profiles based at least in part uponthe telematics data received from the plurality of contributing partiessuch that telematics data associated with one user are included in onedata profile of the plurality of data profiles; generating a pluralityof user profiles such that each user profile of the plurality of userprofiles includes user information of the user to which a data profileof the plurality of data profiles corresponds; listing the plurality ofdata profiles on the marketplace; receiving, from a requesting party(e.g., may be a contributing or a non-contributing party) of theplurality of parties, an information request for a target user profileassociated with a target data profile of the plurality of data profiles;and/or delivering, in response to receiving the information request, thetarget user profile to the requesting party.

In certain embodiments, systems and methods of the present disclosureshares one or more user profiles via a subscription-based universaltelematics marketplace with multiple levels of subscriptions ofdifferent levels of data access. In various examples, a system (e.g.,one including modules to perform a method) and/or a method for sharinguser profiles includes: establishing a marketplace accessible to aplurality of subscribed parties (e.g., insurance companies, car rentalcompanies, vehicle manufacturing companies, autonomous driving firms,shared ride companies, housing firms, banks, government agencies, etc.),each subscribed party having one of a first level subscription, a secondlevel subscription, and a third level subscription; receiving sensordata corresponding to a plurality of vehicle operators from a pluralityof contributing parties; generating, for each vehicle operator of theplurality of vehicle operators, an overall score and one or moresub-scores (e.g., safety score, reliability score, drivingcharacteristic scores such as acceleration, braking, cornering, and/orscore indicative of behavioral insights of the operator) based at leastin part upon the sensor data; generating a plurality of data profilesbased at least in part upon the sensor data received from the pluralityof contributing parties such that sensor data, overall score, and one ormore sub-scores associated with one user are included in one dataprofile of the plurality of data profiles; generating a plurality ofuser profiles such that each user profile of the plurality of userprofiles includes user information of the user to which a data profileof the plurality of data profiles corresponds; listing the plurality ofdata profiles on the marketplace such that: the sensor data of theplurality of data profiles are accessible by one or more first levelsubscribers of the plurality of subscribers having the first levelsubscription; the overall scores of the plurality of data profiles areaccessible by one or more second level subscribers of the plurality ofsubscribers having the second level subscription; and/or the one or moresub-scores of the plurality of data profiles are accessible by one ormore third level subscribers of the plurality of subscribers having thethird level subscription; receiving, from a requesting party (e.g., maybe a contributing or a non-contributing party) of the plurality ofparties, an information request for a target user profile associatedwith a target data profile of the plurality of data profiles; and/ordelivering the target user profile to the requesting party.

In certain embodiments, systems and methods of the present disclosureshares one or more user profiles via an universal telematics marketplacewith multiple levels of subscriptions of different levels accesspolicies. In various examples, a system (e.g., one including modules toperform a method) and/or a method for sharing user profiles includes:establishing a marketplace accessible to a plurality of accessingparties (e.g., insurance companies, car rental companies, vehiclemanufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.), eachaccessing party having one of an open access license and an closedaccess license; receiving telematics data from a plurality ofcontributing parties of the plurality of parties, telematics data beingcollected, by each contributing party of the plurality of contributingparties, via a shared module installed in at least one of aparty-associated hardware collector and a party-associatedprocessor-executable program (e.g., a gaming app, vehicle conditiontracking app, carbon emission monitoring app, on-board vehicle computerOS); generating a plurality of data profiles based at least in part uponthe telematics data received from the plurality of contributing partiessuch that telematics data associated with one user are included in onedata profile of the plurality of data profiles; generating a pluralityof user profiles such that each user profile of the plurality of userprofiles includes user information of the user to which a data profileof the plurality of data profiles corresponds; listing the plurality ofdata profiles on the marketplace; receiving, from a requesting party(e.g., may be a contributing or a non-contributing party) of theplurality of parties, an information request for a target user profileassociated with a target data profile of the plurality of data profiles;determining whether a license type of a contributing party correspondingto the target data profile is a closed access license or an open accesslicense; and/or delivering, in response to determining the license typeof the contributing party is the open access license, the target userprofile to the requesting party.

In certain embodiments, systems and methods of the present disclosureshares one or more user profiles via an universal telematics marketplacewith multiple levels of access policies based at least in part uponlevel-specific license keys. In various examples, a system (e.g., oneincluding modules to perform a method) and/or a method for sharing userprofiles includes: establishing a marketplace accessible to a pluralityof accessing parties (e.g., insurance companies, car rental companies,vehicle manufacturing companies, autonomous driving firms, shared ridecompanies, housing firms, banks, government agencies, etc.), eachaccessing party having one of an open access license and an closedaccess license; receiving telematics data from a plurality ofcontributing parties of the plurality of parties, telematics data beingcollected, by each contributing party of the plurality of contributingparties, via a shared module installed in at least one of aparty-associated hardware collector and a party-associatedprocessor-executable program (e.g., a gaming app, vehicle conditiontracking app, carbon emission monitoring app, on-board vehicle computerOS); generating a plurality of data profiles based at least in part uponthe telematics data received from the plurality of contributing partiessuch that telematics data associated with one user are included in onedata profile of the plurality of data profiles; generating a pluralityof user profiles such that each user profile of the plurality of userprofiles includes user information of the user to which a data profileof the plurality of data profiles corresponds; listing the plurality ofdata profiles on the marketplace; receiving, from a requesting party(e.g., may be a contributing or a non-contributing party) of theplurality of parties, an information request for a target user profileassociated with a target data profile of the plurality of data profiles,the target data profile including sensor data collected by acontributing party having a closed access license; verifying a licensekey of the requesting party against a license key of the contributingparty; and/or delivering, in response to successfully verifying thelicense key between the requesting party and the contributing party, thetarget user profile to the requesting party.

In various embodiments, systems and methods for sharing user profilescontaining universal telematics data collected using a shared module(e.g., same SDK), systems and methods for sharing user profiles via asubscription-based universal telematics marketplace with differentlevels of subscriptions for different levels of data, systems andmethods for sharing user profiles via a universal telematics marketplacewith different levels of access policies, and/or systems and methods forsharing user profiles via a universal telematics marketplace withdifferent levels of access policies based on license key are modifiedand/or combined with systems and methods for sharing vehicle operatorprofiles based on raw telematics data via a marketplace; systems andmethods for sharing vehicle operator profiles based on operator scorevia a marketplace; systems and methods for sharing vehicle operatorprofiles based on universal operator score via a marketplace; systemsand methods for sharing vehicle operator profiles based onparty-specific or use-specific operator score via a marketplace; systemsand methods for securely sharing vehicle operator profiles based onparty-specific or use-specific operator score via a marketplace with oneor more security measures; systems and methods for sharing vehicleoperator profiles based on predicted party-specific or use-specificoperator score via a marketplace; systems and methods for sharingvehicle operator profiles based on vehicle operator score and one ormore sub-scores via a marketplace; and/or systems and methods forsharing vehicle operator profiles based on tiers of operator scores viaa marketplace.

In various embodiments, third parties such as insurance companies nolonger need to provide a party-specific software application and/orhardware-based telematics data collecting device to their customersand/or candidates to collect telematics data, which may be used fordetermining discounts, rewards, pricing, and/or recommendations. In someexamples, provided to users is a universal collector (e.g., a softwareapplication and/or hardware-based device) configured to collecttelematics data shareable to a plurality of parties, such as parties ofdifferent industries. In certain examples, the universal collector isconfigured to utilize, or provide the telematics data to another devicethat is configured to utilize, the telematics data for one or more usesin addition to providing the telematics data to the plurality ofparties, such as upon request. For example, a universal collector isconfigured to utilize the telematics data for monitoring carbon emissionand/or enhancing user interaction and user experience with a softwareapplication, such as a game, such as a game with a virtual driver.

In various embodiments, systems and methods of the present disclosurefor generating leads for a plurality of parties includes receivinguniversal telematics data, such as one collected using a universalcollector, as input, generating one or more scores, metrics,characteristics, and/or pricing associated with a vehicle operator towhich the telematics data are in association. In some examples, systemsand methods of the present disclosure generate a single score for eachvehicle operator to represent an overall safe driving score and/orreliability score for the vehicle operator. In certain examples, partiesmay select and purchase leads for one or more vehicle operators based atleast in part upon the single score, such that more information, such asmore detailed scores and/or raw telematics data, are provided to theparties who purchased the leads. In various examples, parties may selectvehicle operators blindly based solely on their scores, such as via aselection of a score tier, from which telematics data and/or operatorscores associated with one vehicle operator categorized to that scoretier are shared with the purchasing party. In some examples, scorethresholds may be placed by the requesting parties to limit candidatevehicle operators to only ones having operator scores greater than orequal to the score thresholds.

In various embodiments, systems and methods of the present disclosuregenerate party-specific and/or use-specific scores associated withvehicle operators using party-provided models for generating scores fora particular use associated with the party. In certain examples, theparty-provided models may be a full version or a stripped-down versionfor generating preliminary scores.

In various examples, systems and methods of the present disclosure, byproviding operator scores instead of raw telematics data, help reducenetwork load and cost, processing load and cost, and/or storage size andcost, for requesting parties. In some examples, security measures may beimplemented to help ensure party-provided models are used securely. Forexample, via the use of read-only (or use-only) access, audit key, andusage log visible to the model-providing parties.

In some embodiments, systems and methods of the present disclosuregenerate party-specific and/or use-specific scores associated withvehicle operators using predictive models trained using artificialintelligence, such as by taking the input (e.g., sensor data) and output(e.g., score, price) from parties, for generating scores for aparticular use and/or a party.

In some embodiments, systems and methods of the present disclosuredirectly provide collected telematics data to requesters or purchaserssuch that the requesters or purchasers may take telematics data as inputfor their own score-generating models.

In various embodiments, systems and methods of the present disclosureprovide telematics data collected, such as after labeling, such as inthe form of operator scores, on a marketplace configured to be accessedby a plurality of parties, such as including parties from differentindustries. In certain examples, the marketplace is an auctionmarketplace configured to take bids for leads and share telematics datato the winning bids. In some examples, the auction marketplace isconfigured to provide the winning bid a time slot (e.g., minutes, hours,or days) to select vehicle operators to obtain telematics dataassociated with the selected vehicle operators. In certain examples,multiple winning bids are selected, and each requesting party associatedwith each winning bid can select one or more vehicle operators to obtaintelematics data associated with the selected one or more vehicleoperators. In some examples, the highest winning bid receivesinformation associated with a vehicle operator in a first time slot,then the second-highest winning bid receives the same information in asecond time slot following the first time slot. This may be referred toas time-division bidding.

In certain examples, systems and methods of the present disclosureprovide, upon request of a party as part of the auction, additionalinformation such as personal history, preliminary scores, score ranges,prior to the bidding stage of the auctioning, such as in exchange for adisclosure fee.

In various examples, when the winning requesting party associated withthe winning bid obtains a list of vehicle operators satisfying one ormore criteria.

In various examples, systems and methods of the present disclosuregenerate and provide scores specific to a user and/or a party such thatthe scores are informative to the party specifically for a particularuse. For example, systems and methods generate and provide a safetyscore associated with a vehicle operator for insurance companies, carrental companies, and/or (highway) safety agencies. In another example,systems and methods generate and provide a payment reliability scoreassociated with a vehicle operator for parties considering the vehicleoperator as a customer. In another example, systems and methods generateand provide behavioral scores associated with the vehicle operator forcompanies considering the vehicle operator as a customer. For example,in addition to credit check and background check, landlords, real estateagents and property managers can request a behavioral score generatedbased on telematics data to help with decision whether the prospectiverenter or buyer would be a good selection. In various examples, systemsand methods generate and provide vehicle type-specific scores forautomobile manufacturers for improving safety of future products.

In some examples, systems and methods of the present disclosure monitordriving behaviors of vehicle operators, such as including trackingbehavioral changes as a function of which party the vehicle operator isin association with. In some examples, systems and methods evaluatemultiple business models, such as ones adopted by multiple organizationsfor generating premiums, discounts, and/or rewards for its customers,based on driving behavior indicated by the telematics data.

In various embodiments, systems and methods of the present disclosureprovide a software development kit (SDK) for collecting telematics datato multiple parties to be shared across platforms of various uses (e.g.,gaming, vehicle condition tracking, carbon emission monitoring). Forexample, multiple software application developed using the shared SDK,when in association (e.g., installed onto a mobile device) with avehicle operator, only one copy of the telematics data collectionprogram may be run with others suspended to save processing power andreduce redundancy. Data collected are shared across the different uses,such as to enhance user interaction and user experience with theparticular use (e.g., app, game). In some examples, a shared SDK canhelp produce uniformed output of a shared format across multiple partiescollecting telematics data via their corresponding data collector (e.g.,software and/or hardware).

In various embodiments, systems and methods of the present disclosurecollects telematics data from any device installed with the SDK, such asfrom mobile devices installed with software applications including theSDK and/or from automobiles installed with the SDK on their on-boardcomputers.

In certain embodiments, systems and methods of the present disclosureprovide a telematics database configured to be subscribed by a pluralityof parties (e.g., companies, organizations, agencies, individuals), suchas in a limited license (or limited subscription) or in an exclusivelicense (or full subscription).

In some examples, parties with limited licenses to the database maycollect telematics data from its customers using a universal collector(e.g., end-users of data collecting software applications and/orhardware) for the party's own use, and the data collected by partieswith limited licenses are available to the plurality of partiessubscribed (or has license) to the database, regardless of whether thelicense was limited or exclusive.

In some examples, parties with exclusive licenses to the database maycollect telematics data from its customers using a universal collector(e.g., end-users of data collecting software applications and/orhardware) for the party's own use, and exclude all other partiessubscribed (or has license) to the database from accessing its data. Theuniversal collector may be installed with the shared SDK.

In some examples, subscribers of the database can request telematicsdata of one or more vehicle operators from one or more other partiessubscribed to the database directly, and the parties receiving therequests may choose to reject or authorize the data share.

In some examples, the database is configured to allow subscribers who donot contribute telematics data to the database, but instead only consumeby requesting telematics data, such as via a consumption-only license orsubscription.

In certain examples, a subscriber in an industry, such as banking, thatdoes not collect telematics data of its customers (e.g., who are on thedatabase because telematics data were collected from them via other oneor more software applications and/or hardware associated with othersubscribers to the database), can request customer information (e.g.,behavioral scores) associated with its customers from the database, thecustomer information being generated based at least in part upontelematics data.

In various embodiments, systems and methods of the present disclosureprovide a driving telematics platform configured to be accessed bymultiple parties, such as insurance companies, and configured to collecttelematics data from a plurality of users, such as via softwareapplications and/or collection hardware, such as in a uniform dataformat, such as via a shared SDK.

One or More Examples of Machine Learning According to VariousEmbodiments

According to some embodiments, a processor or a processing element maybe trained using supervised machine learning and/or unsupervised machinelearning, and the machine learning may employ an artificial neuralnetwork, which, for example, may be a convolutional neural network, arecurrent neural network, a deep learning neural network, areinforcement learning module or program, or a combined learning moduleor program that learns in two or more fields or areas of interest.Machine learning may involve identifying and recognizing patterns inexisting data in order to facilitate making predictions for subsequentdata. Models may be created based upon example inputs in order to makevalid and reliable predictions for novel inputs.

According to certain embodiments, machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as images, object statistics and information, historical estimates,and/or actual repair costs. The machine learning programs may utilizedeep learning algorithms that may be primarily focused on patternrecognition and may be trained after processing multiple examples. Themachine learning programs may include Bayesian Program Learning (BPL),voice recognition and synthesis, image or object recognition, opticalcharacter recognition, and/or natural language processing. The machinelearning programs may also include natural language processing, semanticanalysis, automatic reasoning, and/or other types of machine learning.

According to some embodiments, supervised machine learning techniquesand/or unsupervised machine learning techniques may be used. Insupervised machine learning, a processing element may be provided withexample inputs and their associated outputs and may seek to discover ageneral rule that maps inputs to outputs, so that when subsequent novelinputs are provided the processing element may, based upon thediscovered rule, accurately predict the correct output. In unsupervisedmachine learning, the processing element may need to find its ownstructure in unlabeled example inputs.

One or More Examples of Modules According to Various Embodiments

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a non-transitory, machine-readable medium) or hardware. In hardware,the routines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computing systems (e.g., a standalone,client or server computing system) or one or more hardware modules of acomputing system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that may be permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that may betemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it may becommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment, or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

Additional Considerations According to Various Embodiments

In some examples, some or all components of various embodiments of thepresent disclosure each are, individually and/or in combination with atleast another component, implemented using one or more softwarecomponents, one or more hardware components, and/or one or morecombinations of software and hardware components. As an example, some orall components of various embodiments of the present disclosure eachare, individually and/or in combination with at least another component,implemented in one or more circuits, such as one or more analog circuitsand/or one or more digital circuits. For example, while the embodimentsdescribed above refer to particular features, the scope of the presentdisclosure also includes embodiments having different combinations offeatures and embodiments that do not include all of the describedfeatures. As an example, various embodiments and/or examples of thepresent disclosure can be combined.

Additionally, the methods and systems described herein may beimplemented on many different types of processing devices by programcode comprising program instructions that are executable by the deviceprocessing subsystem. The software program instructions may includesource code, object code, machine code, or any other stored data that isoperable to cause a processing system to perform the methods andoperations described herein. Certain implementations may also be used,however, such as firmware or even appropriately designed hardwareconfigured to perform the methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, datainput, data output, intermediate data results, final data results) maybe stored and implemented in one or more different types ofcomputer-implemented data stores, such as different types of storagedevices and programming constructs (e.g., RAM, ROM, EEPROM, Flashmemory, flat files, databases, programming data structures, programmingvariables, IF-THEN (or similar type) statement constructs, applicationprogramming interface). It is noted that data structures describeformats for use in organizing and storing data in databases, programs,memory, or other computer-readable media for use by a computer program.

The systems and methods may be provided on many different types ofcomputer-readable media including computer storage mechanisms (e.g.,CD-ROM, diskette, RAM, flash memory, computer's hard drive, DVD) thatcontain instructions (e.g., software) for use in execution by aprocessor to perform the methods' operations and implement the systemsdescribed herein. The computer components, software modules, functions,data stores and data structures described herein may be connecteddirectly or indirectly to each other in order to allow the flow of dataneeded for their operations. It is also noted that a module or processorincludes a unit of code that performs a software operation, and can beimplemented for example as a subroutine unit of code, or as a softwarefunction unit of code, or as an object (as in an object-orientedparadigm), or as an applet, or in a computer script language, or asanother type of computer code. The software components and/orfunctionality may be located on a single computer or distributed acrossmultiple computers depending upon the situation at hand.

The computing system can include client devices and servers. A clientdevice and server are generally remote from each other and typicallyinteract through a communication network. The relationship of clientdevice and server arises by virtue of computer programs running on therespective computers and having a client device-server relationship toeach other.

This specification contains many specifics for particular embodiments.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations, one or more features from a combination can in some casesbe removed from the combination, and a combination may, for example, bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in a particular order, thisshould not be understood as requiring that such operations be performedin the particular order shown or in sequential order, or that allillustrated operations be performed, to achieve desirable results. Incertain circumstances, multitasking and parallel processing may beadvantageous. Moreover, the separation of various system components inthe embodiments described above should not be understood as requiringsuch separation in all embodiments, and it should be understood that thedescribed program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a non-transitory, machine-readable medium) or hardware. In hardware,the routines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computing systems (e.g., a standalone,client or server computing system) or one or more hardware modules of acomputing system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that may be permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that may betemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it may becommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment, or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

Although specific embodiments of the present disclosure have beendescribed, it will be understood by those of skill in the art that thereare other embodiments that are equivalent to the described embodiments.Accordingly, it is to be understood that the present disclosure is notto be limited by the specific illustrated embodiments.

1. A computer-implemented method for data management, thecomputer-implemented method comprising: collecting a plurality ofpersonal data sets associated with a plurality of vehicle operatorscontinually; collecting a plurality of sensor data sets associated withthe plurality of vehicle operators continually via one or more sensingmodules; for each vehicle operator of the plurality of vehicleoperators: generating and continually updating an operator profileincluding the personal data set associated with the vehicle operator;determining and continually updating one or more telematics inferencesbased at least in part upon the sensor data set associated with thevehicle operator; generating and continually updating a data profileincluding the one or more telematics inferences associated with thevehicle operator; and listing and continually updating the data profileonto a telematics marketplace to be accessible by a plurality ofmarketplace participants; receiving, from a plurality of bidders of theplurality of marketplace participants, a plurality of conditional bidsfor a target operator profile associated with a target data profileselected from the listed data profiles of the plurality of vehicleoperators, each conditional bid of the plurality of conditional bidsincluding one or more conditional payments and one or more paymentconditions; determining, for each conditional bid of the plurality ofconditional bids, a predicted bid-generated profit or a predictedbid-generated revenue; determining, based at least in part upon thepredicted profit or predicted bid-generated revenue, a winning bid andan associated winning bidder; and transmitting the target operatorprofile to the winning bidder.
 2. The computer-implemented method ofclaim 1, wherein, the determining the winning bid includes: determiningthe winning bid as the bid of the plurality of conditional bidders whichhas the highest predicted bid-generated profit for a marketplace entity.3. The computer-implemented method of claim 1, wherein, the determiningthe predicted bid-generated profit includes: determining a predictedbid-generated revenue and a predicted bid-generated costs; subtractingthe predicted bid-generated revenue by the predicted bid-generatedcosts.
 4. The computer-implemented method of claim 1, wherein, thedetermining the predicted bid-generated profit includes: determining apredicted user retention duration; and determining the winning bid asthe bid of the plurality of conditional bids which has the highestpredicted long-term bid-generated profit for the full duration of thepredicted user pretention duration.
 5. The computer-implemented methodof claim 1, wherein, the determining the predicted bid-generated profitincludes: determining a predicted user retention duration; anddetermining the winning bid as the bid of the plurality of conditionalbids which has the highest predicted period-specific bid-generatedprofit for a period of interest predetermined by the marketplace entity.6. The computer-implemented method of claim 1, wherein, the determiningthe predicted bid-generated profit includes: determining, for eachpayment condition of the one or more payment conditions, a likelihood ofcondition fulfillment.
 7. The computer-implemented method of claim 6,wherein, the determining the predicted bid-generated profit includes:multiplying, for each payment condition of the one or more paymentconditions, the likelihood of condition fulfillment and an associatedconditional payment of the one or more conditional payments.
 8. Thecomputer-implemented method of claim 1, wherein: the one or moreconditional payments includes a first conditional payment and a secondconditional payment; the one or more conditional payment conditionsincludes a first payment condition and a second payment condition; thefirst conditional payment is withheld from completion at least until thefirst payment condition is satisfied; the second conditional payment iswithheld from completion at least until the first payment condition andthe second payment condition are satisfied.
 9. The computer-implementedmethod of claim 1, wherein: the one or more sensing modules includes acommon module used by a plurality of mobile applications; the commonmodule is a software module or a common hardware module; each vehicleoperator uses at least one mobile application of the plurality of mobileapplications.
 10. A computing system for data management, the computingsystem comprising: one or more processors; and a memory storinginstructions that, upon execution by the one or more processors, causethe computing system to perform one or more processes including:collecting a plurality of personal data sets associated with a pluralityof vehicle operators continually; collecting a plurality of sensor datasets associated with the plurality of vehicle operators continually viaone or more sensing modules; for each vehicle operator of the pluralityof vehicle operators: generating and continually updating an operatorprofile including the personal data set associated with the vehicleoperator; determining and continually updating one or more telematicsinferences based at least in part upon the sensor data set associatedwith the vehicle operator; generating and continually updating a dataprofile including the one or more telematics inferences associated withthe vehicle operator; and listing and continually updating the dataprofile onto a telematics marketplace to be accessible by a plurality ofmarketplace participants; receiving, from a plurality of bidders of theplurality of marketplace participants, a plurality of conditional bidsfor a target operator profile associated with a target data profileselected from the listed data profiles of the plurality of vehicleoperators, each conditional bid of the plurality of conditional bidsincluding one or more conditional payments and one or more paymentconditions; determining, for each conditional bid of the plurality ofconditional bids, a predicted bid-generated profit or a predictedbid-generated revenue; determining, based at least in part upon thepredicted profit or predicted bid-generated revenue, a winning bid andan associated winning bidder; and transmitting the target operatorprofile to the winning bidder.
 11. The computer system of claim 10,wherein, the determining the winning bid includes: determining thewinning bid as the bid of the plurality of conditional bidders which hasthe highest predicted bid-generated profit for a marketplace entity. 12.The computer system of claim 10, wherein, the determining the predictedbid-generated profit includes: determining a predicted bid-generatedrevenue and a predicted bid-generated costs; subtracting the predictedbid-generated revenue by the predicted bid-generated costs.
 13. Thecomputer system of claim 10, wherein, the determining the predictedbid-generated profit includes: determining a predicted user retentionduration; and determining the winning bid as the bid of the plurality ofconditional bids which has the highest predicted long-term bid-generatedprofit for the full duration of the predicted user pretention duration.14. The computer system of claim 10, wherein, the determining thepredicted bid-generated profit includes: determining a predicted userretention duration; and determining the winning bid as the bid of theplurality of conditional bids which has the highest predictedperiod-specific bid-generated profit for a period of interestpredetermined by the marketplace entity.
 15. The computer system ofclaim 10, wherein, the determining the predicted bid-generated profitincludes: determining, for each payment condition of the one or morepayment conditions, a likelihood of condition fulfillment.
 16. Thecomputer system of claim 15, wherein, the determining the predictedbid-generated profit includes: multiplying, for each payment conditionof the one or more payment conditions, the likelihood of conditionfulfillment and an associated conditional payment of the one or moreconditional payments.
 17. The computer system of claim 10, wherein: theone or more conditional payments includes a first conditional paymentand a second conditional payment; the one or more conditional paymentconditions includes a first payment condition and a second paymentcondition; the first conditional payment is withheld from completion atleast until the first payment condition is satisfied; the secondconditional payment is withheld from completion at least until the firstpayment condition and the second payment condition are satisfied. 18.The computer system of claim 10, wherein: the one or more sensingmodules includes a common module used by a plurality of mobileapplications; the common module is a software module or a commonhardware module; each vehicle operator uses at least one mobileapplication of the plurality of mobile applications.
 19. Anon-transitory computer-readable medium storing instructions for datamanagement, the instructions upon execution by one or more processors ofa computing system, cause the computing system to perform one or moreprocesses including: collecting a plurality of personal data setsassociated with a plurality of vehicle operators continually; collectinga plurality of sensor data sets associated with the plurality of vehicleoperators continually via one or more sensing modules; for each vehicleoperator of the plurality of vehicle operators: generating andcontinually updating an operator profile including the personal data setassociated with the vehicle operator; determining and continuallyupdating one or more telematics inferences based at least in part uponthe sensor data set associated with the vehicle operator; generating andcontinually updating a data profile including the one or more telematicsinferences associated with the vehicle operator; and listing andcontinually updating the data profile onto a telematics marketplace tobe accessible by a plurality of marketplace participants; receiving,from a plurality of bidders of the plurality of marketplaceparticipants, a plurality of conditional bids for a target operatorprofile associated with a target data profile selected from the listeddata profiles of the plurality of vehicle operators, each conditionalbid of the plurality of conditional bids including one or moreconditional payments and one or more payment conditions; determining,for each conditional bid of the plurality of conditional bids, apredicted bid-generated profit or a predicted bid-generated revenue;determining, based at least in part upon the predicted profit orpredicted bid-generated revenue, a winning bid and an associated winningbidder; and transmitting the target operator profile to the winningbidder.
 20. The non-transitory computer-readable medium of claim 19,wherein, the determining the winning bid includes: determining thewinning bid as the bid of the plurality of conditional bidders which hasthe highest predicted bid-generated profit for a marketplace entity.